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Meng X, Yang D, Jin H, Xu H, Lu J, Liu Z, Wang Z, Wang L, Yang Z. MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden. Abdom Radiol (NY) 2025; 50:1822-1830. [PMID: 39417854 DOI: 10.1007/s00261-024-04547-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 08/16/2024] [Accepted: 08/19/2024] [Indexed: 10/19/2024]
Abstract
PURPOSE To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H). METHODS A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly divided into training and testing cohorts in a ratio of 7:3. Radiomics features were extracted from sagittal T2-weighted images and contrast-enhanced T1-weighted images. Then, the logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms were used to construct radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance of each model, and decision curve analysis was used to determine their clinical application value. RESULTS Four radiomics features were selected to build the radiomics models. The three models had similar performance, achieving 0.771 (LR), 0.892 (RF), and 0.738 (SVM) in the training cohort, and 0.787 (LR), 0.798 (RF), and 0.777 (SVM) in the testing cohort. The decision curve demonstrated the good clinical application value of the LR model. CONCLUSIONS The MRI-based radiomics models demonstrated moderate predictive ability for TMB-H EC and thus may be a tool for preoperative, noninvasive prediction of TMB-H EC.
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Affiliation(s)
- Xuxu Meng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Dawei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - He Jin
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jun Lu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenhao Liu
- Department of Radiology, Affiliated Hospital of Changzhi Institute of Traditional Chinese Medicine, Changzhi, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Liang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
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Wang X, Deng C, Kong R, Gong Z, Dai H, Song Y, Wu Y, Bi G, Ai C, Bi Q. Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma. Acad Radiol 2025; 32:1476-1487. [PMID: 39368914 DOI: 10.1016/j.acra.2024.09.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Revised: 09/13/2024] [Accepted: 09/16/2024] [Indexed: 10/07/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the validity of multiparametric MRI-based intratumoral and peritumoral habitat imaging for predicting cervical stromal invasion (CSI) in patients with early-stage endometrial carcinoma (EC) and to compare the performance of structural and functional habitats. MATERIALS AND METHODS The preoperative MRI and clinical data of 680 patients with early-stage EC from three centers were retrospectively analyzed. Based on cohort-level, gaussian mixture model (GMM) algorithm was used for habitat clustering of MRI images. Structural habitats were clustered using T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI), and functional habitats were clustered using apparent diffusion coefficient (ADC) mapping and CE-T1WI. Habitat parameters were extracted from four volumes of interest (VOIs): intratumoral regions (ROI), peritumoral loops of 3 mm dilation (L3), intratumoral regions + peritumoral loops of 3 mm dilation (R3), and peritumoral loops of 3 mm dilation + peritumoral loops of 3 mm erosion (DE3). Clinical-habitat models were constructed by combining clinical independent predictors and optimal habitat models. The model performance was evaluated by the area under the curve (AUC). RESULTS Deep myometrial invasion (DMI) was an independent predictor. L3 models showed the best performance for both structural and functional habitats, and the L3 functional habitat model had the highest average AUC (0.807) in external test groups, and the average AUC increased to 0.815 when combing with the clinical independent predictor. CONCLUSION Multiparametric MRI-based intratumoral and peritumoral habitat imaging provides a noninvasive approach to predict CSI in EC patients. The combination of the clinical predictor with the L3 functional habitat model improved predictive performance.
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Affiliation(s)
- Xianhong Wang
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.); Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B)
| | - Cheng Deng
- Department of Radiology, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650101, China (C.D.)
| | - Ruize Kong
- Department of Vascular Surgery, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (R.K.); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Zhimei Gong
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Hongying Dai
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Yang Song
- MR Research Collaboration, Siemens Healthineers, Shanghai 201318, China (Y.S.)
| | - Yunzhu Wu
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China (Y.W.)
| | - Guoli Bi
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.)
| | - Conghui Ai
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China (C.A.)
| | - Qiu Bi
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, Yunnan 650032, China (X.W., Z.G., H.D., G.B., Q.B); The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan 650500, China (X.W., R.K., Z.G., H.D., G.B., Q.B.).
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Wang X, Bi Q, Deng C, Wang Y, Miao Y, Kong R, Chen J, Li C, Liu X, Gong X, Zhang Y, Bi G. Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma. Abdom Radiol (NY) 2025; 50:1414-1425. [PMID: 39276192 DOI: 10.1007/s00261-024-04577-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 09/06/2024] [Accepted: 09/09/2024] [Indexed: 09/16/2024]
Abstract
OBJECTIVE To develop and compare various preoperative cervical stromal invasion (CSI) prediction models, including radiomics, three-dimensional (3D) deep transfer learning (DTL), and integrated models, using single-sequence and multiparametric MRI. METHODS Data from 466 early-stage endometrial carcinoma (EC) patients from three centers were collected. Radiomics models were constructed based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) mapping, contrast-enhanced T1-weighted imaging (CE-T1WI), and four combined sequences as well as 3D DTL models. Two integrated models were created using ensemble and stacking algorithms based on optimal radiomics and DTL models. Model performance and clinical benefits were assessed using area under the curve (AUC), decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination index (IDI), and the Delong test for model comparisons. RESULTS Multiparametric MRI models were superior to single-sequence models for radiomics or DTL models. Ensemble and stacking integrated models displayed excellent performance. The stacking model had the highest average AUC (0.908) and accuracy (0.883) in external validation groups 1 and 2 (AUC = 0.965 and 0.851, respectively) and emerged as the best predictive model for CSI. All models significantly outperformed the radiologist (P < 0.05). In terms of net benefits, all models demonstrated favorable outcomes in DCA, NRI, and IDI, with the stacking model yielding the highest net benefit. CONCLUSION Multiparametric MRI-based radiomics combined with 3D DTL can be used to noninvasively predict CSI in EC patients with greater diagnostic accuracy than the radiologist. Stacking integrated models showed significant potential utility in predicting CSI. Which helps to provide new treatment strategy for clinicians to treat early-stage EC patients.
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Affiliation(s)
- Xianhong Wang
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Qiu Bi
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Cheng Deng
- Department of Radiology, the Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yaoxin Wang
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, the Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Yunbo Miao
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of Geriatrie Medicine, the First People's Hospital of Yunnan Province, Kunming, China
| | - Ruize Kong
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of Vascular Surgery, the First People's Hospital of Yunnan Province, Kunming, China
| | - Jie Chen
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chenrong Li
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Xiulan Liu
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Xiarong Gong
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Ya Zhang
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Guoli Bi
- The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China.
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Russo L, Bottazzi S, Kocak B, Zormpas-Petridis K, Gui B, Stanzione A, Imbriaco M, Sala E, Cuocolo R, Ponsiglione A. Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools. Eur Radiol 2025; 35:202-214. [PMID: 39014086 PMCID: PMC11632020 DOI: 10.1007/s00330-024-10947-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/15/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024]
Abstract
OBJECTIVE To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). METHODS We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. RESULTS Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9-14) and METRICS score of 67.6% (IQR, 58.8-76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. CONCLUSIONS Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. CLINICAL RELEVANCE STATEMENT Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. KEY POINTS The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.
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Affiliation(s)
- Luca Russo
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Bottazzi
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey
| | - Konstantinos Zormpas-Petridis
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Benedetta Gui
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Evis Sala
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
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Song J, Jiang X, Lu Y, Zhang A, Luo C, Cheng W, Duan S, Qu F, Wu F, Chen T. Multi-modality MRI radiomics phenotypes in intermediate-high risk endometrial cancer: correlations with histopathology and prognosis. Jpn J Radiol 2025; 43:68-77. [PMID: 39254904 DOI: 10.1007/s11604-024-01654-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Accepted: 08/28/2024] [Indexed: 09/11/2024]
Abstract
OBJECTIVES This study aimed to identify the magnetic resonance imaging (MRI)-based radiomics phenotypes of intermediate-to-high-risk endometrial cancers (ECs), explore their association with histopathologic features, and compare their prognostic ability with the International Federation of Gynecology and Obstetrics (FIGO) stage. METHODS This study retrospectively recruited 355 patients with pathologically confirmed EC from 01/2016 to 06/2023. 166(46.8%) were classified as intermediate-to-high-risk ECs according to the European Society for Medical Oncology guidelines. Radiomics clustering analysis was performed on preoperative MRI to identify the radiomics phenotype of intermediate-to-high-risk ECs. The association between the radiomics phenotypes and the clinicopathologic information was explored, and the added value in predicting the recurrence was also evaluated using concordance index (C-index). RESULTS Of the included 166 patients (average age 56.83 ± 9.25 years), 23 were recurrent patients. The corresponding tumors in various clusters were assigned to phenotypes 1 and 2. Larger tumor diameter (P < .01), cervical mucosa invasion [30(36.15%) vs 15(18.07%), P = .01], deep myometrial infiltration [51(61.45%) vs 31(37.35%), P = .00], and histologic subtype [17(20.48%) vs 5(6.02%), P = .01] were associated with subtype 1. The risk of recurrence (P = .01) was higher in phenotype 1, and the FIGO stage could further differentiate higher recurrence risk in phenotype 1 (P < .01). The C-index was 0.66 for the radiomics phenotype model, 0.69 for the FIGO stage model, and 0.72 for the combined model. CONCLUSIONS MRI-based radiomics consensus clustering enabled the identification of associations between radiomics features and histopathologic features in intermediate-to-high-risk EC. The FIGO stage could further elevate the prediction ability of recurrence risk.
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Affiliation(s)
- Jiacheng Song
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, China
| | - Xiaoting Jiang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, China
| | - Yao Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, China
| | - Aining Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, China
| | - Chengyan Luo
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Wenjun Cheng
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shaofeng Duan
- Central Research Institute, UIH Group, Shanghai, China
| | - Feifei Qu
- MR Research Collaboration, Siemens Healthineers, Shanghai, China
| | - Feiyun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, China.
| | - Ting Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, China.
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Kido A, Himoto Y, Kurata Y, Minamiguchi S, Nakamoto Y. Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023. J Magn Reson Imaging 2024; 60:1225-1242. [PMID: 38146775 DOI: 10.1002/jmri.29161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 12/27/2023] Open
Abstract
The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Aki Kido
- Department Radiology, Toyama University Hospital, Toyama, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yasuhisa Kurata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | | | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
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Wei ZY, Zhang Z, Zhao DL, Zhao WM, Meng YG. Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer. World J Clin Cases 2024; 12:5908-5921. [PMID: 39286374 PMCID: PMC11287501 DOI: 10.12998/wjcc.v12.i26.5908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/19/2024] [Accepted: 07/03/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. METHODS The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators. RESULTS Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature. CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.
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Affiliation(s)
- Zhi-Yao Wei
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Zhe Zhang
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Dong-Li Zhao
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Wen-Ming Zhao
- National Genomics Data Center and Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100700, China
| | - Yuan-Guang Meng
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
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8
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Jin H, Wang Y, Li X, Yang Y, Qi R. Radiomics nomogram for predicting chemo-immunotherapy efficiency in advanced non-small cell lung cancer. Sci Rep 2024; 14:20788. [PMID: 39242619 PMCID: PMC11379930 DOI: 10.1038/s41598-024-63415-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 05/28/2024] [Indexed: 09/09/2024] Open
Abstract
This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78-0.92), 0.76 (95% CI 0.68-0.84), and 0.89 (95% CI 0.84-0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86-1.00), 0.79 (95% CI 0.68-0.91), and 0.96 (95% CI 0.90-1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72-0.96), 0.75 (95% CI 0.62-0.87), and 0.86 (95% CI 0.75-0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.
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Affiliation(s)
- Hua Jin
- Department of Respiratory Medicine, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Yuchao Wang
- Department of Medical Imaging, Third Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150030, China.
| | - Xushuo Li
- Department of Clinical Laboratory, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Ying Yang
- Department of Clinical Laboratory, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Ruixue Qi
- Department of Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
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Renton M, Fakhriyehasl M, Weiss J, Milosevic M, Laframboise S, Rouzbahman M, Han K, Jhaveri K. Multiparametric MRI radiomics for predicting disease-free survival and high-risk histopathological features for tumor recurrence in endometrial cancer. Front Oncol 2024; 14:1406858. [PMID: 39156704 PMCID: PMC11327158 DOI: 10.3389/fonc.2024.1406858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 07/22/2024] [Indexed: 08/20/2024] Open
Abstract
Background Current preoperative imaging is insufficient to predict survival and tumor recurrence in endometrial cancer (EC), necessitating invasive procedures for risk stratification. Purpose To establish a multiparametric MRI radiomics model for predicting disease-free survival (DFS) and high-risk histopathologic features in EC. Methods This retrospective study included 71 patients with histopathology-proven EC and preoperative MRI over a 10-year period. Clinicopathology data were extracted from health records. Manual MRI segmentation was performed on T2-weighted (T2W), apparent diffusion coefficient (ADC) maps and dynamic contrast-enhanced T1-weighted images (DCE T1WI). Radiomic feature (RF) extraction was performed with PyRadiomics. Associations between RF and histopathologic features were assessed using logistic regression. Associations between DFS and RF or clinicopathologic features were assessed using the Cox proportional hazards model. All RF with univariate analysis p-value < 0.2 were included in elastic net analysis to build radiomic signatures. Results The 3-year DFS rate was 68% (95% CI = 57%-80%). There were no significant clinicopathologic predictors for DFS, whilst the radiomics signature was a strong predictor of DFS (p<0.001, HR 3.62, 95% CI 1.94, 6.75). From 107 RF extracted, significant predictive elastic net radiomic signatures were established for deep myometrial invasion (p=0.0097, OR 4.81, 95% CI 1.46, 15.79), hysterectomy grade (p=0.002, OR 5.12, 95% CI 1.82, 14.45), hysterectomy histology (p=0.0061, OR 18.25, 95% CI 2.29,145.24) and lymphovascular space invasion (p<0.001, OR 5.45, 95% CI 2.07, 14.36). Conclusion Multiparametric MRI radiomics has the potential to create a non-invasive a priori approach to predicting DFS and high-risk histopathologic features in EC.
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Affiliation(s)
- Mary Renton
- The Joint Department of Medical Imaging, University Hospital Network, University of Toronto, Toronto, ON, Canada
| | - Mina Fakhriyehasl
- The Joint Department of Medical Imaging, University Hospital Network, University of Toronto, Toronto, ON, Canada
| | - Jessica Weiss
- Department of Biostatistics, University Hospital Network, Toronto, ON, Canada
| | - Michael Milosevic
- Department of Radiation Oncology, University Hospital Network, Toronto, ON, Canada
| | - Stephane Laframboise
- Department of Gynecologic Oncology, University Hospital Network, Toronto, ON, Canada
| | - Marjan Rouzbahman
- Department of Laboratory Medicine and Pathobiology, University Hospital Network, Toronto, ON, Canada
| | - Kathy Han
- Department of Gynecologic Oncology, University Hospital Network, Toronto, ON, Canada
| | - Kartik Jhaveri
- The Joint Department of Medical Imaging, University Hospital Network, University of Toronto, Toronto, ON, Canada
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10
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Yan R, Qin S, Xu J, Zhao W, Xin P, Xing X, Lang N. A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study. Cancer Imaging 2024; 24:100. [PMID: 39085930 PMCID: PMC11293005 DOI: 10.1186/s40644-024-00743-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 07/24/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Accurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC. METHODS Patients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2Dintra and 3Dintra), peritumoral (2Dperi and 3Dperi), and combined models (2Dintra + peri and 3Dintra + peri) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong's test. RESULTS No significant differences in AUC were observed between the 2Dintra and 3Dintra models, or the 2Dperi and 3Dperi models in all prediction tasks (P > 0.05). Significant difference was observed between the 3Dintra and 3Dperi models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3Dintra + peri models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3Dintra model in both the training and validation cohorts (P < 0.05). CONCLUSIONS Comparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.
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Affiliation(s)
- Ruixin Yan
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Siyuan Qin
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Jiajia Xu
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Weili Zhao
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Peijin Xin
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Xiaoying Xing
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
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11
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Espedal H, Fasmer KE, Berg HF, Lyngstad JM, Schilling T, Krakstad C, Haldorsen IS. MRI radiomics captures early treatment response in patient-derived organoid endometrial cancer mouse models. Front Oncol 2024; 14:1334541. [PMID: 38774411 PMCID: PMC11106402 DOI: 10.3389/fonc.2024.1334541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/23/2024] [Indexed: 05/24/2024] Open
Abstract
Background Radiomics can capture microscale information in medical images beyond what is visible to the naked human eye. Using a clinically relevant mouse model for endometrial cancer, the objective of this study was to develop and validate a radiomic signature (RS) predicting response to standard chemotherapy. Methods Mice orthotopically implanted with a patient-derived grade 3 endometrioid endometrial cancer organoid model (O-PDX) were allocated to chemotherapy (combined paclitaxel/carboplatin, n=11) or saline/control (n=13). During tumor progression, the mice underwent weekly T2-weighted (T2w) magnetic resonance imaging (MRI). Segmentation of primary tumor volume (vMRI) allowed extraction of radiomic features from whole-volume tumor masks. A radiomic model for predicting treatment response was derived employing least absolute shrinkage and selection operator (LASSO) statistics at endpoint images in the orthotopic O-PDX (RS_O), and subsequently applied on the earlier study timepoints (RS_O at baseline, and week 1-3). For external validation, the radiomic model was tested in a separate T2w-MRI dataset on segmented whole-volume subcutaneous tumors (RS_S) from the same O-PDX model, imaged at three timepoints (baseline, day 3 and day 10/endpoint) after start of chemotherapy (n=8 tumors) or saline/control (n=8 tumors). Results The RS_O yielded rapidly increasing area under the receiver operating characteristic (ROC) curves (AUCs) for predicting treatment response from baseline until endpoint; AUC=0.38 (baseline); 0.80 (week 1), 0.85 (week 2), 0.96 (week 3) and 1.0 (endpoint). In comparison, vMRI yielded AUCs of 0.37 (baseline); 0.69 (w1); 0.83 (week 2); 0.92 (week 3) and 0.97 (endpoint). When tested in the external validation dataset, RS_S yielded high accuracy for predicting treatment response at day10/endpoint (AUC=0.85) and tended to yield higher AUC than vMRI (AUC=0.78, p=0.18). Neither RS_S nor vMRI predicted response at day 3 in the external validation set (AUC=0.56 for both). Conclusions We have developed and validated a radiomic signature that was able to capture chemotherapeutic treatment response both in an O-PDX and in a subcutaneous endometrial cancer mouse model. This study supports the promising role of preclinical imaging including radiomic tumor profiling to assess early treatment response in endometrial cancer models.
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Affiliation(s)
- Heidi Espedal
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Western Australia National Imaging Facility, Centre for Microscopy, Characterization and Analysis, University of Western Australia, Perth, WA, Australia
| | - Kristine E. Fasmer
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Hege F. Berg
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway
| | - Jenny M. Lyngstad
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Tomke Schilling
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Camilla Krakstad
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway
| | - Ingfrid S. Haldorsen
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
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12
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Fang R, Lin N, Weng S, Liu K, Chen X, Cao D. Multiparametric MRI radiomics improves preoperative diagnostic performance for local staging in patients with endometrial cancer. Abdom Radiol (NY) 2024; 49:875-887. [PMID: 38189937 DOI: 10.1007/s00261-023-04149-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024]
Abstract
PURPOSE To determine whether multiparametric magnetic resonance imaging (MRI) radiomics-based machine learning methods can improve preoperative local staging in patients with endometrial cancer (EC). METHODS Data of patients with histologically confirmed EC who underwent preoperative MRI were retrospectively analyzed and divided into a training or test set. Radiomic features extracted from multiparametric MR images were used to train and test the prediction of deep myometrial invasion (DMI) and cervical stromal invasion (CSI). Two radiologists assessed the presence of DMI and CSI on conventional MR images. A combined model incorporating a radiomic signature and conventional MR images was constructed and presented as a nomogram. Performance of the predictive models was assessed using the area under curve (AUC) in the receiver operating curve analysis and pairwise comparison using DeLong's test with Bonferroni correction. RESULTS This study included 198 women (training set = 138, test set = 60). Conventional MRI achieved AUCs of 0.837 and 0.799 for detecting DMI and 0.825 and 0.858 for detecting CSI in the training and test sets, respectively. The nomogram achieved AUCs of 0.928 and 0.869 for detecting DMI and 0.913 and 0.937 for detecting CSI in the training and test sets, respectively. The ability of the nomogram to detect DMI and CSI in the two sets was superior to that of conventional MRI (adjusted p < 0.05), except for the ability to detect CSI in the test set (adjusted p > 0.05). CONCLUSION A nomogram incorporating radiomics signature into conventional MRI improved the efficacy of preoperative local staging of EC.
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Affiliation(s)
- Ruqi Fang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Maternity and Children's Hospital, Fuzhou, 350001, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Obstetrics and Gynecology Hospital, Fuzhou, 350011, Fujian, People's Republic of China
| | - Na Lin
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, People's Republic of China
| | - Shuping Weng
- Department of Radiology, Fujian Provincial Maternity and Children's Hospital, Fuzhou, 350001, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Obstetrics and Gynecology Hospital, Fuzhou, 350011, Fujian, People's Republic of China
| | - Kaili Liu
- Department of Radiology, Fujian Provincial Maternity and Children's Hospital, Fuzhou, 350001, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Obstetrics and Gynecology Hospital, Fuzhou, 350011, Fujian, People's Republic of China
| | - Xiaping Chen
- Department of Radiology, Fujian Provincial Maternity and Children's Hospital, Fuzhou, 350001, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Obstetrics and Gynecology Hospital, Fuzhou, 350011, Fujian, People's Republic of China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, People's Republic of China.
- Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, People's Republic of China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, People's Republic of China.
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13
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Huang ML, Ren J, Jin ZY, Liu XY, Li Y, He YL, Xue HD. Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:439-456. [PMID: 38349417 DOI: 10.1007/s11547-024-01765-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/03/2024] [Indexed: 03/16/2024]
Abstract
PURPOSE We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC). METHODS Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed. RESULTS Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively. CONCLUSION The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.
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Affiliation(s)
- Meng-Lin Huang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Obstetric and Gynecologic Diseases, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
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Bae H, Rha SE, Kim H, Kang J, Shin YR. Predictive Value of Magnetic Resonance Imaging in Risk Stratification and Molecular Classification of Endometrial Cancer. Cancers (Basel) 2024; 16:921. [PMID: 38473283 DOI: 10.3390/cancers16050921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/27/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
This study evaluated the magnetic resonance imaging (MRI) findings of endometrial cancer (EC) patients and identified differences based on risk group and molecular classification. The study involved a total of 175 EC patients. The MRI data were retrospectively reviewed and compared based on the risk of recurrence. Additionally, the associations between imaging phenotypes and genomic signatures were assessed. The low-risk and non-low-risk groups (intermediate, high-intermediate, high, metastatic) showed significant differences in tumor diameter (p < 0.001), signal intensity and heterogeneity on diffusion-weighted imaging (DWI) (p = 0.003), deep myometrial invasion (involvement of more than 50% of the myometrium), cervical invasion (p < 0.001), extrauterine extension (p = 0.002), and lymphadenopathy (p = 0.003). Greater diffusion restriction and more heterogeneity on DWI were exhibited in the non-low-risk group than in the low-risk group. Deep myometrial invasion, cervical invasion, extrauterine extension, lymphadenopathy, recurrence, and stage discrepancy were more common in the non-low-risk group (p < 0.001). A significant difference in microsatellite stability status was observed in the heterogeneity of the contrast-enhanced T1-weighted images (p = 0.027). However, no significant differences were found in MRI parameters related to TP53 mutation. MRI features can be valuable predictors for differentiating risk groups in patients with EC. However, further investigations are needed to explore the imaging markers based on molecular classification.
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Affiliation(s)
- Hanna Bae
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 14662, Republic of Korea
| | - Sung Eun Rha
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 14662, Republic of Korea
| | - Hokun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 14662, Republic of Korea
| | - Jun Kang
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 14662, Republic of Korea
| | - Yu Ri Shin
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 14662, Republic of Korea
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Meng H, Sun YF, Zhang Y, Yu YN, Wang J, Wang JN, Xue LY, Yin XP. Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:81-91. [PMID: 38343262 PMCID: PMC10976915 DOI: 10.1007/s10278-023-00936-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 03/02/2024]
Abstract
Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend to evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) for risk stratification and staging of early-stage EC. The study included 155 patients who underwent MRI examinations prior to surgery and were pathologically diagnosed with early-stage EC between January, 2020, and September, 2022. Three-dimensional radiomics features were extracted from segmented tumor images captured by MRI scans (including T2WI, CE-T1WI delayed phase, and ADC), with 1521 features extracted from each of the three modalities. Then, using five-fold cross-validation and a multilayer perceptron algorithm, these features were filtered using Pearson's correlation coefficient to develop a prediction model for risk stratification and staging of EC. The performance of each model was assessed by analyzing ROC curves and calculating the AUC, accuracy, sensitivity, and specificity. In terms of risk stratification, the CE-T1 sequence demonstrated the highest predictive accuracy of 0.858 ± 0.025 and an AUC of 0.878 ± 0.042 among the three sequences. However, combining all three sequences resulted in enhanced predictive accuracy, reaching 0.881 ± 0.040, with a corresponding increase in the AUC to 0.862 ± 0.069. In the context of staging, the utilization of a combination involving T2WI with CE-T1WI led to a notably elevated predictive accuracy of 0.956 ± 0.020, surpassing the accuracy achieved when employing any singular feature. Correspondingly, the AUC was 0.979 ± 0.022. When incorporating all three sequences concurrently, the predictive accuracy reached 0.956 ± 0.000, accompanied by an AUC of 0.986 ± 0.007. It is noteworthy that this level of accuracy surpassed that of the radiologist, which stood at 0.832. The MRI radiomics model has the potential to accurately predict the risk stratification and early staging of EC.
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Affiliation(s)
- Huan Meng
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Yu-Feng Sun
- College of Quality and Technical Supervision, Hebei University, Lianchi District, No. 180, Wusi East Road, Baoding, 071000, China
| | - Yu Zhang
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Ya-Nan Yu
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Jing Wang
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Jia-Ning Wang
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, Lianchi District, No. 180, Wusi East Road, Baoding, 071000, China.
| | - Xiao-Ping Yin
- Department of Radiology, Hebei Key Laboratory of precise imaging of inflammation related tumors, Affiliated Hospital of Hebei University, Lianchi District, No. 212, Eastern Yuhua Road, Baoding, 071000, China.
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Leo E, Stanzione A, Miele M, Cuocolo R, Sica G, Scaglione M, Camera L, Maurea S, Mainenti PP. Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows. J Clin Med 2023; 13:226. [PMID: 38202233 PMCID: PMC10779496 DOI: 10.3390/jcm13010226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 12/23/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.
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Affiliation(s)
- Elisabetta Leo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Mariaelena Miele
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Luigi Camera
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Council of Research (CNR), 80131 Naples, Italy
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Yan B, Zhao T, Li Z, Ren J, Zhang Y. An MR-based radiomics nomogram including information from the peritumoral region to predict deep myometrial invasion in stage I endometrioid adenocarcinoma: a preliminary study. Br J Radiol 2023; 96:20230026. [PMID: 37751166 PMCID: PMC10607389 DOI: 10.1259/bjr.20230026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE To develop and validate an MR-based radiomics nomogram combining different imaging sequences (ADC mapping and T2 weighted imaging (T2WI)), different tumor regions (combined intra- and peritumoral regions), and different parameters (clinical features, tumor morphological features, and radiomics features) while considering different MR field strengths in predicting deep myometrial invasion (MI) in Stage I endometrioid adenocarcinoma (EEA). METHODS A total of 202 patients were retrospectively analyzed and divided into two cohorts (training cohort, 1.5 T MR, n = 131; validation cohort, 3.0 T MR, n = 71). Axial ADC mapping and T2WI were conducted. Radiomics features were extracted from intra- and peritumoral regions. Least absolute shrinkage and selection operator regression, univariate analysis, and multivariate logistic regression were used to select radiomics features and tumor morphological and clinical parameters. The area under the receiver operator characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and radiomics nomogram. RESULTS Ten radiomics features, 4 morphological parameters and 1 clinical characteristic were selected. The radiomics nomogram achieved good discrimination between the superficial and deep MI cohorts. The AUC was 0.927 (95% confidence interval [CI]: 0.865, 0.967) in the training cohort and 0.921 (95% CI: 0.872, 0.948) in the validation cohort. The specificity and sensitivity were 92.0 and 78.9% in the training cohort and 83.0 and 77.8% in the validation cohort, respectively. CONCLUSION The radiomics nomogram showed good performance in predicting the depth of MI in Stage I EEA before surgery and might be useful for surgical patient management. ADVANCES IN KNOWLEDGE An MR-based radiomics nomogram was useful for predicting deep MI in Stage I EEA patients (AUCtrain = 0.927, AUCvalidation = 0.921). The intra- and peritumoral radiomics features complemented each other. The nomogram was developed and validated with different MR field strengths, suggesting that the model demonstrates good generalizability.
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Affiliation(s)
| | - Tingting Zhao
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China
| | | | | | - Yuchen Zhang
- Department of Nuclear Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China
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Yang J, Cao Y, Zhou F, Li C, Lv J, Li P. Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma. Front Oncol 2023; 13:1231497. [PMID: 37909025 PMCID: PMC10613647 DOI: 10.3389/fonc.2023.1231497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Background Differences exist between high- and low-risk endometrial cancer (EC) in terms of whether lymph node dissection is performed. Factors such as tumor grade, myometrial invasion (MDI), and lymphovascular space invasion (LVSI) in the European Society for Medical Oncology (ESMO), European SocieTy for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) guidelines risk classification can often only be accurately assessed postoperatively. The aim of our study was to estimate the risk classification of patients with endometrial endometrioid adenocarcinoma before surgery and offer individualized treatment plans based on their risk classification. Methods Clinical information and last preoperative pelvic magnetic resonance imaging (MRI) of patients with postoperative pathologically determined endometrial endometrioid adenocarcinoma were collected retrospectively. The region of interest (ROI) was subsequently plotted in T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) MRI scans, and the traditional radiomics features and deep-learning image features were extracted. A final radiomics nomogram model integrating traditional radiomics features, deep learning image features, and clinical information was constructed to distinguish between low- and high-risk patients (based on the 2020 ESMO-ESGO-ESTRO guidelines). The efficacy of the model was evaluated in the training and validation sets of the model. Results We finally included 168 patients from January 1, 2020 to July 29, 2021, of which 95 patients in 2021 were classified as the training set and 73 patients in 2020 were classified as the validation set. In the training set, the area under the curve (AUC) of the radiomics nomogram was 0.923 (95%CI: 0.865-0.980) and in the validation set, the AUC of the radiomics nomogram was 0.842 (95%CI: 0.762-0.923). The nomogram had better predictions than both the traditional radiomics model and the deep-learning radiomics model. Conclusion MRI-based radiomics models can be useful for preoperative risk classification of patients with endometrial endometrioid adenocarcinoma.
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Affiliation(s)
| | | | | | | | | | - Pu Li
- Clinical School of Obstetrics and Gynecology Center, Tianjin Medical University, Tianjin, China
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19
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Yan B, Jia Y, Li Z, Ding C, Lu J, Liu J, Zhang Y. Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region. Acta Radiol 2023; 64:2636-2645. [PMID: 37312525 DOI: 10.1177/02841851231181681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making. PURPOSE To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA). MATERIAL AND METHODS A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts. RESULTS Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUCtrain = 0.919, and AUCvalidation = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively. CONCLUSION The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.
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Affiliation(s)
- Bin Yan
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
- Department of Radiology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, PR China
| | - Yuxia Jia
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, PR China
| | - Zhihao Li
- GE Healthcare China, Xi'an, Shaanxi, PR China
| | - Caixia Ding
- Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, PR China
| | - Jianrong Lu
- Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, PR China
| | - Jixin Liu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, PR China
| | - Yuchen Zhang
- Department of Nuclear Medicine, First Affiliated Hospital of Xi'an Jiaotong University, PR China
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20
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Shen L, Du L, Hu Y, Chen X, Hou Z, Yan Z, Wang X. MRI-based radiomics model for distinguishing Stage I endometrial carcinoma from endometrial polyp: a multicenter study. Acta Radiol 2023; 64:2651-2658. [PMID: 37291882 DOI: 10.1177/02841851231175249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
BACKGROUND Patients with early endometrial carcinoma (EC) have a good prognosis, but it is difficult to distinguish from endometrial polyps (EPs). PURPOSE To develop and assess magnetic resonance imaging (MRI)-based radiomics models for discriminating Stage I EC from EP in a multicenter setting. MATERIAL AND METHODS Patients with Stage I EC (n = 202) and EP (n = 99) who underwent preoperative MRI scans were collected in three centers (seven devices). The images from devices 1-3 were utilized for training and validation, and the images from devices 4-7 were utilized for testing, leading to three models. They were evaluated by the area under the receiver operating characteristic curve (AUC) and metrics including accuracy, sensitivity, and specificity. Two radiologists evaluated the endometrial lesions and compared them with the three models. RESULTS The AUCs of device 1, 2_ada, device 1, 3_ada, and device 2, 3_ada for discriminating Stage I EC from EP were 0.951, 0.912, and 0.896 for the training set, 0.755, 0.928, and 1.000 for the validation set, and 0.883, 0.956, and 0.878 for the external validation set, respectively. The specificity of the three models was higher, but the accuracy and sensitivity were lower than those of radiologists. CONCLUSION Our MRI-based models showed good potential in differentiating Stage I EC from EP and had been validated in multiple centers. Their specificity was higher than that of radiologists and may be used for computer-aided diagnosis in the future to assist clinical diagnosis.
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Affiliation(s)
- Liting Shen
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China
| | - Lixin Du
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen, PR China
| | - Yumin Hu
- Department of Radiology, Lishui Central Hospital, Zhejiang, PR China
| | - Xiaojun Chen
- Department of Radiology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, PR China
| | - Zujun Hou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, PR China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China
| | - Xue Wang
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China
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Wang Y, Chen Z, Liu C, Chu R, Li X, Li M, Yu D, Qiao X, Kong B, Song K. Radiomics-based fertility-sparing treatment in endometrial carcinoma: a review. Insights Imaging 2023; 14:127. [PMID: 37466860 DOI: 10.1186/s13244-023-01473-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 06/25/2023] [Indexed: 07/20/2023] Open
Abstract
In recent years, with the increasing incidence of endometrial carcinoma in women of child-bearing age, to decision of whether to preserve patients' fertility during treatment has become increasingly complex, presenting a formidable challenge for both physicians and patients. Non-fertility-sparing treatment can remove lesions more thoroughly than fertility-sparing treatment. However, patients will permanently lose their fertility. In contrast, fertility-sparing treatment can treat tumors without impairing fertility, but the risk of disease progression is high as compared with non-fertility-sparing treatment. Therefore, it is extremely important to accurately identify patients who are suitable for fertility-sparing treatments. The evaluation of prognostic factors, including myometrial invasion, the presence of lymph node metastases, and histopathological type, is vital for determining whether a patient can receive fertility-sparing treatment. As a non-invasive and quantitative approach, radiomics has the potential to assist radiologists and other clinicians in determining more precise judgments with regard to the above factors by extracting imaging features and establishing predictive models. In this review, we summarized currently available fertility-sparing strategies and reviewed the performance of radiomics in predicting risk factors associated with fertility-sparing treatment. This review aims to assist clinicians in identifying patients suitable for fertility-sparing treatment more accurately and comprehensively and informs more appropriate and rigorous treatment decisions for endometrial cancer patients of child-bearing age.Critical relevance statement: Radiomics is a promising tool that may assist clinicians identify risk factors about fertility-sparing more accurately and comprehensively.
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Affiliation(s)
- Yuanjian Wang
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
| | - Zhongshao Chen
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
| | - Chang Liu
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Ran Chu
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiao Li
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China.
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China.
| | - Mingbao Li
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China.
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China.
| | - Dexin Yu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xu Qiao
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Beihua Kong
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Kun Song
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China.
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China.
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22
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Di Donato V, Kontopantelis E, Cuccu I, Sgamba L, Golia D'Augè T, Pernazza A, Della Rocca C, Manganaro L, Catalano C, Perniola G, Palaia I, Tomao F, Giannini A, Muzii L, Bogani G. Magnetic resonance imaging-radiomics in endometrial cancer: a systematic review and meta-analysis. Int J Gynecol Cancer 2023; 33:1070-1076. [PMID: 37094971 DOI: 10.1136/ijgc-2023-004313] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
OBJECTIVE Endometrial carcinoma is the most common gynecological tumor in developed countries. Clinicopathological factors and molecular subtypes are used to stratify the risk of recurrence and to tailor adjuvant treatment. The present study aimed to assess the role of radiomics analysis in pre-operatively predicting molecular or clinicopathological prognostic factors in patients with endometrial carcinoma. METHODS Literature was searched for publications reporting radiomics analysis in assessing diagnostic performance of MRI for different outcomes. Diagnostic accuracy performance of risk prediction models was pooled using the metandi command in Stata. RESULTS A search of MEDLINE (PubMed) resulted in 153 relevant articles. Fifteen articles met the inclusion criteria, for a total of 3608 patients. MRI showed pooled sensitivity and specificity 0.785 and 0.814, respectively, in predicting high-grade endometrial carcinoma, deep myometrial invasion (pooled sensitivity and specificity 0.743 and 0.816, respectively), lymphovascular space invasion (pooled sensitivity and specificity 0.656 and 0.753, respectively), and nodal metastasis (pooled sensitivity and specificity 0.831 and 0.736, respectively). CONCLUSIONS Pre-operative MRI-radiomics analyses in patients with endometrial carcinoma is a good predictor of tumor grading, deep myometrial invasion, lymphovascular space invasion, and nodal metastasis.
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Affiliation(s)
- Violante Di Donato
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Evangelos Kontopantelis
- Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK
| | - Ilaria Cuccu
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Ludovica Sgamba
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Tullio Golia D'Augè
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Angelina Pernazza
- Department of Medical-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Rome, Italy
| | - Carlo Della Rocca
- Department of Medical-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Rome, Italy
| | - Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Giorgia Perniola
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Innocenza Palaia
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Federica Tomao
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Andrea Giannini
- Department of Medical and Surgical Sciences and Translational Medicine, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Ludovico Muzii
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Giorgio Bogani
- Department of Gynecologic Oncology, IRCCS National Cancer Institute, Milan, Italy
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Panic J, Defeudis A, Balestra G, Giannini V, Rosati S. Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:67-76. [PMID: 37283773 PMCID: PMC10241248 DOI: 10.1109/ojemb.2023.3271455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/18/2023] [Accepted: 04/25/2023] [Indexed: 06/08/2023] Open
Abstract
Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.
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Affiliation(s)
- Jovana Panic
- Department of Surgical Science, and Polytechnic of Turin, Department of Electronics and TelecommunicationsUniversity of Turin10129TurinItaly
| | - Arianna Defeudis
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Gabriella Balestra
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
| | - Valentina Giannini
- Department of Surgical ScienceUniversity of Turin10129TurinItaly
- Candiolo Cancer InstituteFPO-IRCCS10060CandioloItaly
| | - Samanta Rosati
- Department of Electronics and TelecommunicationsPolytechnic of Turin10129TurinItaly
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Zheng T, Pan J, Du D, Liang X, Yi H, Du J, Wu S, Liu L, Shi G. Preoperative assessment of high-grade endometrial cancer using a radiomic signature and clinical indicators. Future Oncol 2023; 19:587-601. [PMID: 37097730 DOI: 10.2217/fon-2022-0631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
Aim: To develop and validate a radiomics-based combined model (ModelRC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, ModelRC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864-0.962), 0.882 (95% CI: 0.779-0.955) and 0.881 (95% CI: 0.815-0.939) for the training, internal validation and external validation sets, respectively. Conclusion: ModelRC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.
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Affiliation(s)
- Tao Zheng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Jiangyang Pan
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Dan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Xin Liang
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Huiling Yi
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Juan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Shuo Wu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Lanxiang Liu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
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A nomogram for preoperative risk stratification based on MR morphological parameters in patients with endometrioid adenocarcinoma. Eur J Radiol 2023; 163:110789. [PMID: 37068415 DOI: 10.1016/j.ejrad.2023.110789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/02/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
PURPOSE To develop and validate a nomogram based on MRI morphological parameters to preoperatively discriminate between low-risk and non-low-risk patients with endometrioid endometrial carcinoma (EEC). METHODS Two hundred eighty-one women with histologically confirmed EEC were divided into training (1.5-T MRI, n = 182) and validation cohorts (3.0-T MRI, n = 99). According to the European Society of Medical Oncology guidelines, the patients were divided into four risk groups: low, intermediate, high-intermediate, and high. Binary classification models were developed (low-risk vs. non-low-risk). Univariate logistic regression (LR) analyses were used to determine which variables to select to build the predictive models. Five classification models were constructed, and the best model was selected. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and nomogram. P < 0.05 indicated a statistically significant difference. RESULTS Age and four morphological parameters (tumor size, tumor volume, maximum anteroposterior tumor diameter on sagittal T2-weighted images (APsag), and tumor area ratio (TAR)) were selected, and the LR model was used to construct an MRI morphological nomogram. The AUCs for the nomogram in predicting a non-low-risk of EEC among patients in the training and validation cohorts were 0.856 (sensitivity = 75.0%, specificity = 83.1%) and 0.849 (sensitivity = 74.6%, specificity = 85.0%), respectively. CONCLUSION An MRI morphological nomogram was developed and achieved high diagnostic performance for classifying low-risk and non-low-risk EEC preoperatively, which could provide support for therapeutic decision-making. Furthermore, our findings indicate that this nomogram is robust in the clinical application of various field strength data.
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Cuccu I, D’Oria O, Sgamba L, De Angelis E, Golia D’Augè T, Turetta C, Di Dio C, Scudo M, Bogani G, Di Donato V, Palaia I, Perniola G, Tomao F, Muzii L, Giannini A. Role of Genomic and Molecular Biology in the Modulation of the Treatment of Endometrial Cancer: Narrative Review and Perspectives. Healthcare (Basel) 2023; 11:healthcare11040571. [PMID: 36833105 PMCID: PMC9957190 DOI: 10.3390/healthcare11040571] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Endometrial cancer (EC) is one of the most common gynecological malignancies in Western countries. Traditionally, loco-reginal dissemination and histological characteristics are the main prognostic factors. Nowadays, molecular and genomic profiling showed exciting results in terms of prognostication. According to the data provided by The Cancer Genome Atlas and other studies, molecular and genomic profiling might be useful in identifying patients al low, intermediate, and high risk of recurrence. However, data regarding the therapeutic value are scant. Several prospective studies are ongoing to identify the most appropriate adjuvant strategy in EC patients, especially for those with positive nodes and low volume disease. The molecular classification has offered the possibility to improve the risk stratification and management of EC. The aim of this review is to focus on the evolution of molecular classification in EC and its impact on the research approach and on clinical management. Molecular and genomic profiling might be useful to tailor the most appropriate adjuvant strategies in apparent early-stage EC.
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Affiliation(s)
- Ilaria Cuccu
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Ottavia D’Oria
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
- Department of Medical and Surgical Sciences and Translational Medicine, Translational Medicine and Oncology, Sapienza University, 00161 Rome, Italy
- Correspondence:
| | - Ludovica Sgamba
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Emanuele De Angelis
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Tullio Golia D’Augè
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Camilla Turetta
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Camilla Di Dio
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Maria Scudo
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Giorgio Bogani
- Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Violante Di Donato
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Innocenza Palaia
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Giorgia Perniola
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Federica Tomao
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Ludovico Muzii
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Andrea Giannini
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
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Liu XF, Yan BC, Li Y, Ma FH, Qiang JW. Radiomics nomogram in aiding preoperatively dilatation and curettage in differentiating type II and type I endometrial cancer. Clin Radiol 2023; 78:e29-e36. [PMID: 36192204 DOI: 10.1016/j.crad.2022.08.139] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 01/21/2023]
Abstract
AIM To established a radiomics nomogram for improving the dilatation and curettage (D&C) result in differentiating type II from type I endometrial cancer (EC) preoperatively. MATERIAL AND METHODS EC patients (n=875) were enrolled retrospectively and divided randomly into a training cohort (n=437) and a test cohort (n=438), according to the ratio of 1:1. Radiomics signatures were extracted and selected from apparent diffusion coefficient (ADC) maps. A multivariate logistic regression analysis was used to identify the independent clinical risk factors. An ADC based-radiomics nomogram was built by integrating the selected radiomics signatures and the independent clinical risk factors. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the radiomics nomogram. The net reclassification index (NRI) and total integrated discrimination index (IDI) were calculated to compare the discrimination performances between the radiomics nomogram and the D&C result. RESULTS Receiver operating characteristic (ROC) curves showed that the clinical risk factors, the D&C, and the ADC based-radiomics nomogram yielded areas under the ROC curves (AUCs) of 0.70 (95% CI: 0.64-0.76), 0.85 (95% CI: 0.80-0.89), and 0.93 (95% CI: 0.90-0.96) in the training cohort and 0.64 (95% CI: 0.57-0.71), 0.82 (95% CI: 0.77-0.87) and 0.91 (95% CI: 0.87-0.95) in the test cohort, respectively. The DCA, NRI, and IDI demonstrated the clinically usefulness of the ADC based-radiomics nomogram. CONCLUSION The ADC-based radiomics nomogram could be used to improve the D&C result in differentiating type II from type I EC preoperatively.
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Affiliation(s)
- X-F Liu
- Department of Radiology, Jinshan Hospital, Fudan University, 201508, Shanghai, China
| | - B-C Yan
- Department of Radiology, Jinshan Hospital, Fudan University, 201508, Shanghai, China
| | - Y Li
- Department of Radiology, Jinshan Hospital, Fudan University, 201508, Shanghai, China.
| | - F-H Ma
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, 200090, Shanghai, China
| | - J-W Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, 201508, Shanghai, China.
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Yue X, He X, He S, Wu J, Fan W, Zhang H, Wang C. Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer. Front Oncol 2023; 13:1081134. [PMID: 36895487 PMCID: PMC9989162 DOI: 10.3389/fonc.2023.1081134] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 01/31/2023] [Indexed: 02/23/2023] Open
Abstract
Background Tumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC. Methods One hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI). Results In total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754-0.920), 0.875 (95% CI: 0.797-0.952), and 0.923 (95% CI: 0.869-0.977) for the training set; 0.857 (95% CI: 0.741-0.973), 0.785 (95% CI: 0.592-0.979), and 0.914 (95% CI: 0.827-0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214-1.061) and 0.657 (0.079-1.394), and IDIs were 0.115 (0.077-0.306) and 0.053 (0.027-0.357) in the training set and validation set, respectively. Conclusion The radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage.
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Affiliation(s)
- Xiaoning Yue
- Department of CT&MRI, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China
| | - Xiaoyu He
- Department of CT&MRI, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China
| | - Shuaijie He
- Department of CT&MRI, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China
| | - Jingjing Wu
- Department of CT&MRI, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China
| | - Wei Fan
- Department of CT&MRI, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China
| | - Haijun Zhang
- Department of Pathology, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China
| | - Chengwei Wang
- Department of CT&MRI, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China
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Zhu Y, Hu Y, Shi J, Wei X, Song Y, Tang C, Zhang W. Plasma Thioredoxin Reductase as a Potential Biomarker for Gynecologic Cancer. Technol Cancer Res Treat 2023; 22:15330338231184995. [PMID: 37365925 DOI: 10.1177/15330338231184995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND According to previous literatures, plasma thioredoxin reductase (TrxR) level was significantly elevated in various malignant tumors and serve as a potential biomarker for diagnosis and prognostic prediction. However, there is little awareness of the clinical value of plasma TrxR in gynecologic malignancies. In the present study, we aim to evaluate the diagnostic accuracy of plasma TrxR in gynecologic cancer and explore its role in treatment surveillance. METHODS We retrospectively enrolled 134 patients with gynecologic cancer and 79 patients with benign gynecologic disease. The difference of plasma TrxR activity and tumor markers level between two groups was compared using Mann-Whitney U test. By detecting pretreatment and post-treatment level of TrxR and conventional tumor markers, we further assessed the change trend of them with the Wilcoxon signed-ranks test. RESULTS Compared with benign control [5.7 (5, 6.6) U/mL], statistically significant increase of TrxR activity was observed in gynecologic cancer group [8.4 (7.25, 9.825) U/mL] (P < .0001), regardless of age and stage. On the basis of receiver operating characteristic (ROC) curves, we found plasma TrxR shows the highest diagnostic efficacy for distinguishing malignancy with benign disease, with an area under the curve (AUC) of 0.823 (95% confidence interval [CI] = 0.767-0.878), in the whole cohort. Besides, patients receiving treatment previously [8 (6.5, 9) U/mL] had a decreased TrxR level relative to treatment-native patients [9.9 (8.6, 10.85) U/mL]. Furthermore, follow-up data showed that plasma TrxR level would be evidently decreased after two courses of antitumor therapy (P < .0001), which is consistent with the downward trend of conventional tumor markers. CONCLUSION Collectively, all these results demonstrated plasma TrxR is an effective parameter for gynecologic cancer diagnosis and concurrently acts as a promising biomarker for treatment response assessment.
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Affiliation(s)
- Yinxing Zhu
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Department of Radiation Oncology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Yixuan Hu
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Junfeng Shi
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaowei Wei
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yaqi Song
- Department of Radiation Oncology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Cuiju Tang
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wenwen Zhang
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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Celli V, Guerreri M, Pernazza A, Cuccu I, Palaia I, Tomao F, Di Donato V, Pricolo P, Ercolani G, Ciulla S, Colombo N, Leopizzi M, Di Maio V, Faiella E, Santucci D, Soda P, Cordelli E, Perniola G, Gui B, Rizzo S, Della Rocca C, Petralia G, Catalano C, Manganaro L. MRI- and Histologic-Molecular-Based Radio-Genomics Nomogram for Preoperative Assessment of Risk Classes in Endometrial Cancer. Cancers (Basel) 2022; 14:5881. [PMID: 36497362 PMCID: PMC9739755 DOI: 10.3390/cancers14235881] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/17/2022] [Accepted: 11/24/2022] [Indexed: 12/02/2022] Open
Abstract
High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performed. We aimed to develop MRI-based radio-genomic models able to preoperatively assess lymph-vascular space invasion (LVSI) and discriminate between low- and high-risk EC according to the ESMO-ESGO-ESTRO 2020 guidelines, which include molecular risk classification proposed by "ProMisE". This is a retrospective, multicentric study that included 64 women with EC who underwent 3T-MRI before a hysterectomy. Radiomics features were extracted from T2WI images and apparent diffusion coefficient maps (ADC) after manual segmentation of the gross tumor volume. We constructed a multiple logistic regression approach from the most relevant radiomic features to distinguish between low- and high-risk classes under the ESMO-ESGO-ESTRO 2020 guidelines. A similar approach was taken to assess LVSI. Model diagnostic performance was assessed via ROC curves, accuracy, sensitivity and specificity on training and test sets. The LVSI predictive model used a single feature from ADC as a predictor; the risk class model used two features as predictors from both ADC and T2WI. The low-risk predictive model showed an AUC of 0.74 with an accuracy, sensitivity, and specificity of 0.74, 0.76, 0.94; the LVSI model showed an AUC of 0.59 with an accuracy, sensitivity, and specificity of 0.60, 0.50, 0.61. MRI-based radio-genomic models are useful for preoperative EC risk stratification and may facilitate therapeutic management.
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Affiliation(s)
- Veronica Celli
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
| | - Michele Guerreri
- Department of Computer Science & Centre for Medical Image Computing, University College London, London WC1E 6BT, UK
- AINOSTICS, Manchester M2 3NG, UK
- Radiomics Core Research Facility, Fondazione Policlinico Universitario “A.Gemelli” IRCCS, 00168 Roma, Italy
| | - Angelina Pernazza
- Department of Medico-Surgical Sciences and Biotechnologies, Polo Pontino-Sapienza University, 04100 Latina, Italy
| | - Ilaria Cuccu
- Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Policlinico Umberto I, Viale del Policlinico 166, 00161 Rome, Italy
| | - Innocenza Palaia
- Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Policlinico Umberto I, Viale del Policlinico 166, 00161 Rome, Italy
| | - Federica Tomao
- Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Policlinico Umberto I, Viale del Policlinico 166, 00161 Rome, Italy
| | - Violante Di Donato
- Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Policlinico Umberto I, Viale del Policlinico 166, 00161 Rome, Italy
| | - Paola Pricolo
- Department of Radiology, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giada Ercolani
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
| | - Sandra Ciulla
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
| | - Nicoletta Colombo
- Division of Gynecologic Oncology, European Institute of Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), 20099 Milano, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, 20126 Monza, Italy
| | - Martina Leopizzi
- Department of Medico-Surgical Sciences and Biotechnologies, Polo Pontino-Sapienza University, 04100 Latina, Italy
| | - Valeria Di Maio
- Department of Medico-Surgical Sciences and Biotechnologies, Polo Pontino-Sapienza University, 04100 Latina, Italy
| | - Eliodoro Faiella
- Medical Oncology Department, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Domiziana Santucci
- Unit of Diagnostic Imaging, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Paolo Soda
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Ermanno Cordelli
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Giorgia Perniola
- Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Policlinico Umberto I, Viale del Policlinico 166, 00161 Rome, Italy
| | - Benedetta Gui
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, 00168 Rome, Italy
| | - Stefania Rizzo
- Clinica di Radiologia EOC, Istituto Imaging della Svizzera Italiana (IIMSI), 6900 Lugano, Switzerland
| | - Carlo Della Rocca
- Department of Medico-Surgical Sciences and Biotechnologies, Polo Pontino-Sapienza University, 04100 Latina, Italy
| | - Giuseppe Petralia
- Department of Radiology, European Institute of Oncology IRCCS, 20141 Milan, Italy
- Precision Imaging and Research Unit, Department of Medical Imaging and Radiation Sciences, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
| | - Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
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Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study. J Pers Med 2022; 12:jpm12111854. [PMID: 36579601 PMCID: PMC9696574 DOI: 10.3390/jpm12111854] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/11/2022] [Accepted: 10/31/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE To develop and validate magnetic resonance (MR) imaging-based radiomics models for high-risk endometrial cancer (EC) prediction preoperatively, to be able to estimate deep myometrial invasion (DMI) and lymphovascular space invasion (LVSI), and to discriminate between low-risk and other categories of risk as proposed by ESGO/ESTRO/ESP (European Society of Gynaecological Oncology-European Society for Radiotherapy & Oncology and European Society of Pathology) guidelines. METHODS This retrospective study included 96 women with EC who underwent 1.5-T MR imaging before surgical staging between April 2009 and May 2019 in two referral centers divided into training (T = 73) and validation cohorts (V = 23). Radiomics features were extracted using the MODDICOM library with manual delineation of whole-tumor volume on MR images (axial T2-weighted). Diagnostic performances of radiomic models were evaluated by area under the receiver operating characteristic (ROC) curve in training (AUCT) and validation (AUCV) cohorts by using a subset of the most relevant texture features tested individually in univariate analysis using Wilcoxon-Mann-Whitney. RESULTS A total of 228 radiomics features were extracted and ultimately limited to 38 for DMI, 29 for LVSI, and 15 for risk-classes prediction for logistic radiomic modeling. Whole-tumor radiomic models yielded an AUCT/AUCV of 0.85/0.68 in DMI estimation, 0.92/0.81 in LVSI prediction, and 0.84/0.76 for differentiating low-risk vs other risk classes (intermediate/high-intermediate/high). CONCLUSION MRI-based radiomics has great potential in developing advanced prognostication in EC.
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Bi Q, Wang Y, Deng Y, Liu Y, Pan Y, Song Y, Wu Y, Wu K. Different multiparametric MRI-based radiomics models for differentiating stage IA endometrial cancer from benign endometrial lesions: A multicenter study. Front Oncol 2022; 12:939930. [PMID: 35992858 PMCID: PMC9389365 DOI: 10.3389/fonc.2022.939930] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
PURPOSE The aim of this study was to evaluate the value of different multiparametric MRI-based radiomics models in differentiating stage IA endometrial cancer (EC) from benign endometrial lesions. METHODS The data of patients with endometrial lesions from two centers were collected. The radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) map, and late contrast-enhanced T1-weighted imaging (LCE-T1WI). After data dimension reduction and feature selection, nine machine learning algorithms were conducted to determine which was the optimal radiomics model for differential diagnosis. The univariate analyses and logistic regression (LR) were performed to reduce valueless clinical parameters and to develop the clinical model. A nomogram using the radscores combined with clinical parameters was developed. Two integrated models were obtained respectively by the ensemble strategy and stacking algorithm based on the clinical model and optimal radiomics model. The area under the curve (AUC), clinical decisive curve (CDC), net reclassification index (NRI), and integrated discrimination index (IDI) were used to evaluate the performance and clinical benefits of the models. RESULTS A total of 371 patients were incorporated. The LR model was the optimal radiomics model with the highest average AUC (0.854) and accuracy (0.802) in the internal and external validation groups (AUC = 0.910 and 0.798, respectively), and outperformed the clinical model (AUC = 0.739 and 0.592, respectively) or the radiologist (AUC = 0.768 and 0.628, respectively). The nomogram (AUC = 0.917 and 0.802, respectively) achieved better discrimination performance than the optimal radiomics model in two validation groups. The stacking model (AUC = 0.915) and ensemble model (AUC = 0.918) had a similar performance compared with the nomogram in the internal validation group, whereas the AUCs of the stacking model (AUC = 0.792) and ensemble model (AUC = 0.794) were lower than those of the nomogram and radiomics model in the external validation group. According to the CDC, NRI, and IDI, the optimal radiomics model, nomogram, stacking model, and ensemble model achieved good net benefits. CONCLUSIONS Multiparametric MRI-based radiomics models can non-invasively differentiate stage IA EC from benign endometrial lesions, and LR is the best machine learning algorithm. The nomogram presents excellent and stable diagnostic efficiency.
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Affiliation(s)
- Qiu Bi
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Yaoxin Wang
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Yuchen Deng
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Yang Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuanrui Pan
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers, Shanghai, China
| | - Yunzhu Wu
- MR Scientific Marketing, Siemens Healthineers, Shanghai, China
| | - Kunhua Wu
- Department of MRI, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
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Liu XF, Lu JJ, Li MD, Li Y, Zeng AR, Qiang JW. Prediction of pre-eclampsia by using radiomics nomogram from gestational hypertension patients. Front Neurosci 2022; 16:961348. [PMID: 35992933 PMCID: PMC9389207 DOI: 10.3389/fnins.2022.961348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 07/19/2022] [Indexed: 11/17/2022] Open
Abstract
Background Pre-eclampsia (PE) is the main cause of death in maternal and prenatal morbidity. No effective clinical tools could be used for the prediction of PE. A radiomics nomogram based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps was established to predict PE from gestational hypertension (GH). Materials and methods A total of 138 patients with hypertensive disorders of pregnancy were continuously enrolled in the study prospectively, namely, 58 patients with PE and 80 patients with GH. The patients were randomly divided into a training cohort (n = 97) and a test cohort (n = 41). Radiomics features were extracted from DWI and ADC maps. The radiomics signature was constructed using a least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. A radiomics nomogram was developed by combining the radiomics signature with the selected clinical risk factors. The area under the receiver operating characteristic (ROC) curves (AUC), specificity, sensitivity, accuracy, positive predictive value, and negative predictive values of the radiomics signature, clinical risk factors, and radiomics nomogram were calculated. Decision curve analysis (DCA) was performed to determine the clinical usefulness of the radiomics nomogram. Results The LASSO analysis finally included 11 radiomics features, which were defined as the radiomics signature. The individualized prediction nomogram was constructed by integrating the radiomics signature, maternal age, and body mass index (BMI). The nomogram exhibited a good performance both in the training cohort [AUC of 0.89 (95% CI, 0.82–0.95)] and test cohort [AUC of 0.85 (95% CI, 0.73–0.97)] for predicting PE from GH. The DCA indicated that clinicians and patients could benefit from the use of radiomics nomogram. Conclusion The radiomics nomogram could individually predict PE from GH. The nomogram could be conveniently used to facilitate the treatment decision for clinicians and patients.
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Lefebvre TL, Ueno Y, Dohan A, Chatterjee A, Vallières M, Winter-Reinhold E, Saif S, Levesque IR, Zeng XZ, Forghani R, Seuntjens J, Soyer P, Savadjiev P, Reinhold C. Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer. Radiology 2022; 305:375-386. [PMID: 35819326 DOI: 10.1148/radiol.212873] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the training set, radiomics provided increased performance compared with radiologists' readings for identifying deep MI (balanced accuracy, 86% vs 79%; P = .03), while no evidence of a difference was observed in performance for advanced FIGO stage (80% vs 78%; P = .27). Conclusion Three-dimensional radiomics can stratify patients by using preoperative MRI according to high-risk histopathologic end points in endometrial carcinoma and provide nonsignificantly different or higher performance than radiologists in identifying advanced stage and deep myometrial invasion, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kido and Nishio in this issue.
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Affiliation(s)
- Thierry L Lefebvre
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Yoshiko Ueno
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Anthony Dohan
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Avishek Chatterjee
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Martin Vallières
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Eric Winter-Reinhold
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Sameh Saif
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Ives R Levesque
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Xing Ziggy Zeng
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Reza Forghani
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Jan Seuntjens
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Philippe Soyer
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Peter Savadjiev
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
| | - Caroline Reinhold
- From the Medical Physics Unit, Department of Oncology (T.L.L., A.C., M.V., I.R.L., J.S.), Department of Diagnostic Radiology (Y.U., S.S., R.F., P. Savadjiev, C.R.), and School of Computer Science (P. Savadjiev), McGill University, Montreal General Hospital Site, 1650 Cedar Ave, Montreal, QC, Canada H3G 1A4; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, England (T.L.L.); Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan (Y.U.); Department of Radiology, Cochin Hospital, AP-HP.Centre, Paris, France (A.D., P. Soyer); Université de Paris, Faculté de Médecine, Paris, France (A.D., P. Soyer); Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands (A.C.); Department of Computer Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada (M.V.); Augmented Intelligence & Precision Health Laboratory (E.W.R., R.F., C.R.), Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Obstetrics and Gynecology, McGill University Health Centre, Montreal, Quebec, Canada (X.Z.Z.); and Montreal Imaging Experts, Montreal, Quebec, Canada (R.F., C.R.)
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Kido A, Nishio M. MRI-based Radiomics Models for Pretreatment Risk Stratification of Endometrial Cancer. Radiology 2022; 305:387-389. [PMID: 35819329 DOI: 10.1148/radiol.221398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Aki Kido
- From the Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Mizuho Nishio
- From the Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
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Shrestha P, Poudyal B, Yadollahi S, E. Wright D, V. Gregory A, D. Warner J, Korfiatis P, C. Green I, L. Rassier S, Mariani A, Kim B, K. Laughlin-Tommaso S, L. Kline T. A systematic review on the use of artificial intelligence in gynecologic imaging – Background, state of the art, and future directions. Gynecol Oncol 2022; 166:596-605. [PMID: 35914978 DOI: 10.1016/j.ygyno.2022.07.024] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE Machine learning, deep learning, and artificial intelligence (AI) are terms that have made their way into nearly all areas of medicine. In the case of medical imaging, these methods have become the state of the art in nearly all areas from image reconstruction to image processing and automated analysis. In contrast to other areas, such as brain and breast imaging, the impacts of AI have not been as strongly felt in gynecologic imaging. In this review article, we: (i) provide a background of clinically relevant AI concepts, (ii) describe methods and approaches in computer vision, and (iii) highlight prior work related to image classification tasks utilizing AI approaches in gynecologic imaging. DATA SOURCES A comprehensive search of several databases from each database's inception to March 18th, 2021, English language, was conducted. The databases included Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations, and Daily, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, and Ovid Cochrane Database of Systematic Reviews and ClinicalTrials.gov. METHODS OF STUDY SELECTION We performed an extensive literature review with 61 articles curated by three reviewers and subsequent sorting by specialists using specific inclusion and exclusion criteria. TABULATION, INTEGRATION, AND RESULTS We summarize the literature grouped by each of the three most common gynecologic malignancies: endometrial, cervical, and ovarian. For each, a brief introduction encapsulating the AI methods, imaging modalities, and clinical parameters in the selected articles is presented. We conclude with a discussion of current developments, trends and limitations, and suggest directions for future study. CONCLUSION This review article should prove useful for collaborative teams performing research studies targeted at the incorporation of radiological imaging and AI methods into gynecological clinical practice.
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Zhao M, Wen F, Shi J, Song J, Zhao J, Song Q, Lai Q, Luo Y, Yu T, Jiang X, Jiang W, Dong Y. MRI-based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO I stage endometrial carcinoma. Med Phys 2022; 49:6505-6516. [PMID: 35758644 DOI: 10.1002/mp.15835] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/11/2022] [Accepted: 06/10/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Endometrial carcinoma (EC) is one of the most common gynecological malignancies with an increasing incidence, and an accurate preoperative diagnosis of deep myometrial invasion (DMI) is crucial for personalized treatment. OBJECTIVE To determine the predictive value of an MRI-based radiomics nomogram for the presence of DMI in FIGO I stage EC. METHODS We retrospectively collected 163 patients with pathologically confirmed stage I EC from two centers and divided all samples into a training group (center 1) and a validation group (center 2). Clinical and routine imaging indicators were analyzed by logistical regression to construct a conventional diagnostic model (M1). Radiomics features extracted from the axial T2-weighted (T2W) and axial contrast-enhanced T1-weighted (CE-T1W) images were treated with the intraclass correlation coefficient, Mann-Whitney U test, least absolute shrinkage and selection operator (LASSO), and logistic regression analysis with Akaike information criterion (AIC) to build a combined radiomics signature (M2). A nomogram (M3) was constructed by M1 and M2. Calibration and decision curves were drawn to evaluate the nomogram in the training and validation cohorts. The diagnostic performance of each indicator and model was evaluated by the area under the receiver operating characteristic curve (AUC). RESULT The four most significant radiomics features were finally selected from the CE-T1W MRI. For the diagnosis of DMI, the AUCT /AUCV of M1 was 0.798/0.738, the AUCT /AUCV of M2 was 0.880/0.852, and the AUCT /AUCV of M3 was 0.936/0.871 in the training and validation groups, respectively. The calibration curves showed that M3 was in good agreement with the ideal values. The decision curve analysis suggested potential clinical application values of the nomogram. CONCLUSION A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO I stage EC. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mingli Zhao
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Feng Wen
- Radiology Department, Shengjing Hospital, China Medical University, Shenyang, Liaoning, 110122, China
| | - Jiaxin Shi
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, 110001, China
| | - Jing Song
- Radiology Department, Shengjing Hospital, China Medical University, Shenyang, Liaoning, 110122, China
| | - Jiaqi Zhao
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Qingling Song
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Qingyuan Lai
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Yahong Luo
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Tao Yu
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, 110001, China
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Yue Dong
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
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Zhang J, Zhang Q, Wang T, Song Y, Yu X, Xie L, Chen Y, Ouyang H. Multimodal MRI-Based Radiomics-Clinical Model for Preoperatively Differentiating Concurrent Endometrial Carcinoma From Atypical Endometrial Hyperplasia. Front Oncol 2022; 12:887546. [PMID: 35692806 PMCID: PMC9186045 DOI: 10.3389/fonc.2022.887546] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives To develop and validate a radiomics model based on multimodal MRI combining clinical information for preoperative distinguishing concurrent endometrial carcinoma (CEC) from atypical endometrial hyperplasia (AEH). Materials and Methods A total of 122 patients (78 AEH and 44 CEC) who underwent preoperative MRI were enrolled in this retrospective study. Radiomics features were extracted based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. After feature reduction by minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithm, single-modal and multimodal radiomics signatures, clinical model, and radiomics-clinical model were constructed using logistic regression. Receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis were used to assess the models. Results The combined radiomics signature of T2WI, DWI, and ADC maps showed better discrimination ability than either alone. The radiomics-clinical model consisting of multimodal radiomics features, endometrial thickness >11mm, and nulliparity status achieved the highest area under the ROC curve (AUC) of 0.932 (95% confidential interval [CI]: 0.880-0.984), bootstrap corrected AUC of 0.922 in the training set, and AUC of 0.942 (95% CI: 0.852-1.000) in the validation set. Subgroup analysis further revealed that this model performed well for patients with preoperative endometrial biopsy consistent and inconsistent with postoperative pathologic data (consistent group, F1-score = 0.865; inconsistent group, F1-score = 0.900). Conclusions The radiomics model, which incorporates multimodal MRI and clinical information, might be used to preoperatively differentiate CEC from AEH, especially for patients with under- or over-estimated preoperative endometrial biopsy.
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Affiliation(s)
- Jieying Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qi Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tingting Wang
- Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Song
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoduo Yu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lizhi Xie
- MR Research China, GE Healthcare, Beijing, China
| | - Yan Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Han Ouyang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Liu XF, Yan BC, Li Y, Ma FH, Qiang JW. Radiomics Nomogram in Assisting Lymphadenectomy Decisions by Predicting Lymph Node Metastasis in Early-Stage Endometrial Cancer. Front Oncol 2022; 12:894918. [PMID: 35712484 PMCID: PMC9192943 DOI: 10.3389/fonc.2022.894918] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/04/2022] [Indexed: 12/24/2022] Open
Abstract
Background Lymph node metastasis (LNM) is an important risk factor affecting treatment strategy and prognosis for endometrial cancer (EC) patients. A radiomics nomogram was established in assisting lymphadenectomy decisions preoperatively by predicting LNM status in early-stage EC patients. Methods A total of 707 retrospective clinical early-stage EC patients were enrolled and randomly divided into a training cohort and a test cohort. Radiomics features were extracted from MR imaging. Three models were built, including a guideline-recommended clinical model (grade 1-2 endometrioid tumors by dilatation and curettage and less than 50% myometrial invasion on MRI without cervical infiltration), a radiomics model (selected radiomics features), and a radiomics nomogram model (combing the selected radiomics features, myometrial invasion on MRI, and cancer antigen 125). The predictive performance of the three models was assessed by the area under the receiver operating characteristic (ROC) curves (AUC). The clinical decision curves, net reclassification index (NRI), and total integrated discrimination index (IDI) based on the total included patients to assess the clinical benefit of the clinical model and the radiomics nomogram were calculated. Results The predictive ability of the clinical model, the radiomics model, and the radiomics nomogram between LNM and non-LNM were 0.66 [95% CI: 0.55-0.77], 0.82 [95% CI: 0.74-0.90], and 0.85 [95% CI: 0.77-0.93] in the training cohort, and 0.67 [95% CI: 0.56-0.78], 0.81 [95% CI: 0.72-0.90], and 0.83 [95% CI: 0.74-0.92] in the test cohort, respectively. The decision curve analysis, NRI (1.06 [95% CI: 0.81-1.32]), and IDI (0.05 [95% CI: 0.03-0.07]) demonstrated the clinical usefulness of the radiomics nomogram. Conclusions The predictive radiomics nomogram could be conveniently used for individualized prediction of LNM and assisting lymphadenectomy decisions in early-stage EC patients.
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Affiliation(s)
- Xue-Fei Liu
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Bi-Cong Yan
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Ying Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Jin-Wei Qiang, ; Ying Li,
| | - Feng-Hua Ma
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | - Jin-Wei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Jin-Wei Qiang, ; Ying Li,
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Mainenti PP, Stanzione A, Cuocolo R, Grosso RD, Danzi R, Romeo V, Raffone A, Sardo ADS, Giordano E, Travaglino A, Insabato L, Scaglione M, Maurea S, Brunetti A. MRI radiomics: a machine learning approach for the risk stratification of endometrial cancer patients. Eur J Radiol 2022; 149:110226. [PMID: 35231806 DOI: 10.1016/j.ejrad.2022.110226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/28/2022] [Accepted: 02/17/2022] [Indexed: 12/31/2022]
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Aimagambetova G, Terzic S, Laganà AS, Bapayeva G, la Fleur P, Terzic M. Contemporary Fertility-Sparing Management Options of Early Stage Endometrioid Endometrial Cancer in Young Nulliparous Patients. J Clin Med 2021; 11:196. [PMID: 35011935 PMCID: PMC8746136 DOI: 10.3390/jcm11010196] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/16/2021] [Accepted: 12/25/2021] [Indexed: 12/15/2022] Open
Abstract
Incidence of endometrial cancer (EC) has been increasing in recent years, especially in high-income countries. The disease commonly affects peri- and postmenopausal women; however, about 5% of women are diagnosed with EC in their reproductive age. Due to both the increasing incidence of EC among reproductive age women and trends to delayed childbearing, fertility-sparing treatment for young patients with EC has become extremely important for researchers and practitioners. Because the classic treatment with total hysterectomy and bilateral saplingo-oophorectomy is not an appropriate approach for young women demanding fertility preservation, several fertility-sparing options have been developed and summarized in this review. Utilization of different medications and their combination (progestagens, gonadotropin releasing hormones analogues, and metformin in different formulations) are tested and found as efficient for fertility-sparing treatment. New minimally invasive surgical techniques, combined with progestagens, are also confirmed as valuable. There are many novel conservative and surgical treatment approaches under investigation. Assuming that molecular biomarkers can be both diagnostic and prognostic to assist in prediction of response to a certain therapy, prognostic risk groups' stratification along with specific biomarkers' identification will ensure low recurrence and decrease mortality rates in young women with EC.
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Affiliation(s)
- Gulzhanat Aimagambetova
- Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
| | - Sanja Terzic
- Department of Medicine, School of Medicine, Nazarbayev University, Nur-Sultan 010000, Kazakhstan; (S.T.); (P.l.F.); (M.T.)
| | - Antonio Simone Laganà
- Department of Obstetrics and Gynecology, “Filippo Del Ponte” Hospital, University of Insubria, 21100 Varese, Italy;
| | - Gauri Bapayeva
- National Research Center of Mother and Child Health, Clinical Academic Department of Women’s Health, University Medical Center, Nur-Sultan 010000, Kazakhstan;
| | - Philip la Fleur
- Department of Medicine, School of Medicine, Nazarbayev University, Nur-Sultan 010000, Kazakhstan; (S.T.); (P.l.F.); (M.T.)
| | - Milan Terzic
- Department of Medicine, School of Medicine, Nazarbayev University, Nur-Sultan 010000, Kazakhstan; (S.T.); (P.l.F.); (M.T.)
- National Research Center of Mother and Child Health, Clinical Academic Department of Women’s Health, University Medical Center, Nur-Sultan 010000, Kazakhstan;
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
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Hoivik EA, Hodneland E, Dybvik JA, Wagner-Larsen KS, Fasmer KE, Berg HF, Halle MK, Haldorsen IS, Krakstad C. A radiogenomics application for prognostic profiling of endometrial cancer. Commun Biol 2021; 4:1363. [PMID: 34873276 PMCID: PMC8648740 DOI: 10.1038/s42003-021-02894-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 11/09/2021] [Indexed: 12/12/2022] Open
Abstract
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.
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Affiliation(s)
- Erling A Hoivik
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Erlend Hodneland
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Julie A Dybvik
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kari S Wagner-Larsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kristine E Fasmer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Hege F Berg
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Mari K Halle
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Ingfrid S Haldorsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Camilla Krakstad
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
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MRI-based radiomics model for distinguishing endometrial carcinoma from benign mimics: A multicenter study. Eur J Radiol 2021; 146:110072. [PMID: 34861530 DOI: 10.1016/j.ejrad.2021.110072] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/19/2021] [Accepted: 11/22/2021] [Indexed: 12/30/2022]
Abstract
PURPOSE To develop and validate an MRI-based radiomics model for preoperatively distinguishing endometrial carcinoma (EC) with benign mimics in a multicenter setting. METHODS Preoperative MRI scans of EC patients were retrospectively reviewed and divided into training set (158 patients from device 1 in center A), test set #1 (78 patients from device 2 in center A) and test set #2 (109 patients from device 3 in center B). Two radiologists performed manual delineation of tumor segmentation on the map of apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI). The features were extracted and firstly selected using Chi-square test, followed by refining using binary least absolute shrinkage and selection operator (LASSO) regression. The support vector machine (SVM) was employed to build the radiomics model, which is tuned in the training set using 10-fold cross-validation, and then assessed on the test set. Performance of the model was determined by area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1-score. RESULTS Five most informative features are selected from the extracted 3142 features. The SVM with linear kernel was employed to build the radiomics model. The AUCs of the model were 0.989 (95% confidence interval [CI]: 0.968-0.997) for the training set, 0.999 (95% CI: 0.991-1.000) for test set #1, 0.961 (95% CI: 0.902-0.983) for test set #2. Accuracies of the model were 0.937 for the training set (precision: 0.919, recall: 0.963, F1-score: 0.940), 0.974 for test set #1 (precision: 0.949, recall: 1.000, F1-score: 0.974) and 0.908 for test set #2 (precision: 0.899, recall: 0.954, F1-score: 0.925). These results confirmed the efficacy of this model in predicting EC in different centers. CONCLUSION The MRI-based radiomics model showed promising potential for distinguishing EC with benign mimics and might be useful for surgical management of EC.
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Moro F, Bertoldo V, Avesani G, Moruzzi MC, Mascilini F, Bolomini G, Caliolo G, Esposito R, Moroni R, Zannoni GF, Fagotti A, Manfredi R, Scambia G, Testa AC. Fusion imaging in preoperative assessment of extent of disease in patients with advanced ovarian cancer: feasibility and agreement with laparoscopic findings. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2021; 60:256-268. [PMID: 33847427 DOI: 10.1002/uog.24805] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 10/07/2021] [Accepted: 10/13/2021] [Indexed: 05/25/2023]
Abstract
OBJECTIVES Fusion imaging is an emerging technique that combines real-time ultrasound examination with images acquired previously using other modalities, such as computed tomography (CT), magnetic resonance imaging and positron emission tomography. The primary aim of this study was to evaluate the feasibility of fusion imaging in patients with suspicion of ovarian or peritoneal cancer. Secondary aims were: to compare the agreement of findings on fusion imaging, CT alone and ultrasound imaging alone with laparoscopic findings, in the assessment of extent of intra-abdominal disease; and to evaluate the time required for the fusion imaging technique. METHODS Patients with clinical and/or radiographic suspicion of advanced ovarian or peritoneal cancer who were candidates for surgery were enrolled prospectively between December 2019 and September 2020. All patients underwent a CT scan and ultrasound and fusion imaging to evaluate the presence or absence of the following abdominal-cancer features according to the laparoscopy-based scoring model (predictive index value (PIV)): supracolic omental disease, visceral carcinomatosis on the liver, lesser omental carcinomatosis and/or visceral carcinomatosis on the lesser curvature of the stomach and/or spleen, involvement of the paracolic gutter(s) and/or anterior abdominal wall, involvement of the diaphragm and visceral carcinomatosis on the small and/or large bowel (regardless of rectosigmoid involvement). The feasibility of the fusion examination in these patients was evaluated. Agreement of each imaging method (ultrasound, CT and fusion imaging) with laparoscopy (considered as reference standard) was calculated using Cohen's kappa coefficient. RESULTS Fifty-two patients were enrolled into the study. Fusion imaging was feasible in 51 (98%) of these patients (in one patient, it was not possible for technical reasons). Two patients were excluded because laparoscopy was not performed, leaving 49 women in the final analysis. Kappa values for CT, ultrasound and fusion imaging, using laparoscopy as the reference standard, in assessing the PIV parameters were, respectively: 0.781, 0.845 and 0.896 for the great omentum; 0.329, 0.608 and 0.847 for the liver surface; 0.472, 0.549 and 0.756 for the lesser omentum and/or stomach and/or spleen; 0.385, 0.588 and 0.795 for the paracolic gutter(s) and/or anterior abdominal wall; 0.385, 0.497 and 0.657 for the diaphragm; and 0.336, 0.410 and 0.469 for the bowel. The median time needed to perform the fusion examination was 20 (range, 10-40) min. CONCLUSION Fusion of CT images and real-time ultrasound imaging is feasible in patients with suspicion of ovarian or peritoneal cancer and improves the agreement with surgical findings when compared with ultrasound or CT scan alone. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- F Moro
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - V Bertoldo
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - G Avesani
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Rome, Italy
| | - M C Moruzzi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - F Mascilini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - G Bolomini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - G Caliolo
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Rome, Italy
| | - R Esposito
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
| | - R Moroni
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Direzione Scientifica, Rome, Italy
| | - G F Zannoni
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Istituto di Anatomia Patologica, Università Cattolica del Sacro Cuore, Rome, Italy
| | - A Fagotti
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Università Cattolica del Sacro Cuore, Dipartimento Scienze della Vita e di Sanità Pubblica, Rome, Italy
| | - R Manfredi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Rome, Italy
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - G Scambia
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Università Cattolica del Sacro Cuore, Dipartimento Scienze della Vita e di Sanità Pubblica, Rome, Italy
| | - A C Testa
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Rome, Italy
- Università Cattolica del Sacro Cuore, Dipartimento Scienze della Vita e di Sanità Pubblica, Rome, Italy
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Endometrial cancer from early to advanced-stage disease: an update for radiologists. Abdom Radiol (NY) 2021; 46:5325-5336. [PMID: 34297164 DOI: 10.1007/s00261-021-03220-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 01/23/2023]
Abstract
The purpose of this article is to review the current molecular classification of endometrial cancer, the imaging findings in early and advanced disease, and the current management strategies, focusing on the new systemic therapies for advanced EC. In recent years, the management of endometrial cancer has significantly changed. The molecular characterization of endometrial cancer has shed new light into the biologic behavior of this disease, the International Federation of Gynecology and Obstetrics staging system was recently revised, and imaging was formally incorporated in the management of endometrial cancer. Recent genomic analysis of endometrial cancer led to the approval of new molecular-targeted therapies and immune checkpoint inhibitors. Imaging allows assessment of myometrial invasion, cervical stromal extension, lymph node involvement and distant metastases, and has a crucial role for treatment planning. Treatment strategies, which include surgery, radiation and systemic therapies are based on accurate staging and risk stratification.
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Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion. Magn Reson Imaging 2021; 85:161-167. [PMID: 34687853 DOI: 10.1016/j.mri.2021.10.024] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/31/2021] [Accepted: 10/17/2021] [Indexed: 11/22/2022]
Abstract
PURPOSE To evaluate radiomic machine learning (ML) classifiers based on multiparametric magnetic resonance images (MRI) in pretreatment assessment of endometrial cancer (EC) risk factors and to examine effects on radiologists' interpretation of deep myometrial invasion (dMI). METHODS This retrospective study examined 200 consecutive patients with EC during January 2004 -March 2017, divided randomly to Discovery (n = 150) and Test (n = 50) datasets. Radiomic features of tumors were extracted from T2-weighted images, apparent diffusion coefficient map, and contrast enhanced T1-weighed images. Using the Discovery dataset, feature selection and hyperparameter tuning for XGBoost were performed. Ten classifiers were built to predict dMI, histological grade, lymphovascular invasion (LVI), and pelvic/paraaortic lymph node metastasis (PLNM/PALNM), respectively. Using the Test dataset, the diagnostic performances of ten classifiers were assessed by the area under the receiver operator characteristic curve (AUC). Next, four radiologists assessed dMI independently using MRI with a Likert scale before and after referring to inference of the ML classifier for the Test dataset. Then, AUCs obtained before and after reference were compared. RESULTS In the Test dataset, mean AUC of ML classifiers for dMI, histological grade, LVI, PLNM, and PALNM were 0.83, 0.77, 0.81, 0.72, and 0.82. AUCs of all radiologists for dMI (0.83-0.88) were better than or equal to mean AUC of the ML classifier, which showed no statistically significant difference before and after the reference. CONCLUSION Radiomic classifiers showed promise for pretreatment assessment of EC risk factors. Radiologists' inferences outperformed the ML classifier for dMI and showed no improvement by review.
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Kurata Y, Nishio M, Moribata Y, Kido A, Himoto Y, Otani S, Fujimoto K, Yakami M, Minamiguchi S, Mandai M, Nakamoto Y. Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network. Sci Rep 2021; 11:14440. [PMID: 34262088 PMCID: PMC8280152 DOI: 10.1038/s41598-021-93792-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/29/2021] [Indexed: 12/29/2022] Open
Abstract
Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personalized medicine. There have been several radiomics studies for noninvasive risk stratification of EC using MRI. Although tumor segmentation is usually necessary for these studies, manual segmentation is not only labor-intensive but may also be subjective. Therefore, our study aimed to perform the automatic segmentation of EC on MRI with a convolutional neural network. The effect of the input image sequence and batch size on the segmentation performance was also investigated. Of 200 patients with EC, 180 patients were used for training the modified U-net model; 20 patients for testing the segmentation performance and the robustness of automatically extracted radiomics features. Using multi-sequence images and larger batch size was effective for improving segmentation accuracy. The mean Dice similarity coefficient, sensitivity, and positive predictive value of our model for the test set were 0.806, 0.816, and 0.834, respectively. The robustness of automatically extracted first-order and shape-based features was high (median ICC = 0.86 and 0.96, respectively). Other high-order features presented moderate-high robustness (median ICC = 0.57-0.93). Our model could automatically segment EC on MRI and extract radiomics features with high reliability.
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Affiliation(s)
- Yasuhisa Kurata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Mizuho Nishio
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan.
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan.
| | - Yusaku Moribata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
- Preemptive Medicine and Lifestyle-Related Disease Research Center, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Aki Kido
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Satoshi Otani
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Koji Fujimoto
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Masahiro Yakami
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
- Preemptive Medicine and Lifestyle-Related Disease Research Center, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Sachiko Minamiguchi
- Department of Diagnostic Pathology, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Masaki Mandai
- Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan
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Lecointre L, Dana J, Lodi M, Akladios C, Gallix B. Artificial intelligence-based radiomics models in endometrial cancer: A systematic review. Eur J Surg Oncol 2021; 47:2734-2741. [PMID: 34183201 DOI: 10.1016/j.ejso.2021.06.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/03/2021] [Accepted: 06/20/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Radiological preoperative assessment of endometrial cancer (EC) is in some cases not precise enough and its performances improvement could lead to a clinical benefit. Radiomics is a recent field of application of artificial intelligence (AI) in radiology. AIMS To investigate the contribution of radiomics on the radiological preoperative assessment of patients with EC; and to establish a simple and reproducible AI Quality Score applicable to Machine Learning and Deep Learning studies. METHODS We conducted a systematic review of current literature including original articles that studied EC through imaging-based AI techniques. Then, we developed a novel Simplified and Reproducible AI Quality score (SRQS) based on 10 items which ranged to 0 to 20 points in total which focused on clinical relevance, data collection, model design and statistical analysis. SRQS cut-off was defined at 10/20. RESULTS We included 17 articles which studied different radiological parameters such as deep myometrial invasion, lympho-vascular space invasion, lymph nodes involvement, etc. One article was prospective, and the others were retrospective. The predominant technique was magnetic resonance imaging. Two studies developed Deep Learning models, while the others machine learning ones. We evaluated each article with SRQS by 2 independent readers. Finally, we kept only 7 high-quality articles with clinical impact. SRQS was highly reproducible (Kappa = 0.95 IC 95% [0.907-0.988]). CONCLUSION There is currently insufficient evidence on the benefit of radiomics in EC. Nevertheless, this field is promising for future clinical practice. Quality should be a priority when developing these new technologies.
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Affiliation(s)
- Lise Lecointre
- Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France; I-Cube UMR 7357 - Laboratoire des Sciences de L'ingénieur, de L'informatique et de L'imagerie, Université de Strasbourg, Strasbourg, France; Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France.
| | - Jérémy Dana
- Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France; Inserm U1110, Institut de Recherche sur Les Maladies Virales et Hépatiques, Strasbourg, France
| | - Massimo Lodi
- Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Chérif Akladios
- Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Benoît Gallix
- I-Cube UMR 7357 - Laboratoire des Sciences de L'ingénieur, de L'informatique et de L'imagerie, Université de Strasbourg, Strasbourg, France; Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France; Department of Diagnostic Radiology, McGill University, Montreal, Canada
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Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Eid M, Iannicelli E, Laghi A. Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications. Cancers (Basel) 2021; 13:cancers13112681. [PMID: 34072366 PMCID: PMC8197789 DOI: 10.3390/cancers13112681] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 01/08/2023] Open
Abstract
Simple Summary This Part II is an overview of the main applications of Radiomics in oncologic imaging with a focus on diagnosis, prognosis prediction and assessment of response to therapy in thoracic, genito-urinary, breast, neurologic, hematologic and musculoskeletal oncology. In this part II we describe the radiomic applications, limitations and future perspectives for each pre-eminent tumor. In the future, Radiomics could have a pivotal role in management of cancer patients as an imaging tool to support clinicians in decision making process. However, further investigations need to obtain some stable results and to standardize radiomic analysis (i.e., image acquisitions, segmentation and model building) in clinical routine. Abstract Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.
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Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marwen Eid
- Internal Medicine, Northwell Health Staten Island University Hospital, Staten Island, New York, NY 10305, USA;
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-0633775285
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Jiang Y, Sun J, Xia Y, Cheng Y, Xie L, Guo X, Guo Y. Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model. Front Oncol 2021; 11:644994. [PMID: 33937051 PMCID: PMC8086552 DOI: 10.3389/fonc.2021.644994] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 02/11/2021] [Indexed: 12/12/2022] Open
Abstract
Objective: To explore a CT-based radiomics model for preoperative prediction of event-free survival (EFS) in patients with hepatoblastoma and to compare its performance with that of a clinicopathologic model. Patients and Methods: Eighty-eight patients with histologically confirmed hepatoblastoma (mean age: 2.28 ± 2.72 years) were recruited from two institutions between 2002 and 2019 for this retrospective study. They were divided into a training cohort (65 patients from institution A) and a validation cohort (23 patients from institution B). Radiomics features were extracted manually from pretreatment CT images in the portal venous (PV) phase. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to construct a “radiomics signature” and radiomics score (Rad-score) for EFS prediction. Then, a nomogram incorporating the Rad-score, updated staging system, and significant variables of clinicopathologic risk (age, alpha-fetoprotein (AFP) level, histology subtype, tumor diameter) as the radiomic model, clinicopathologic model, and combined clinicopathologic-radiomic model were built for EFS estimation in the training cohort, the performance of which was assessed in an external-validation cohort with respect to clinical usefulness, discrimination, and calibration. Results: Nine survival-relevant features were selected for a radiomics signature and Rad-score building. Multivariable analysis revealed that histology subtype (P = 0.01), PV (P = 0.001) invasion, and metastasis (P = 0.047) were independent risk factors of EFS. Patients were divided into low- and high-risk groups based on the Rad-score with a cutoff of 0.08 according to survival outcome. The radiomics signature-incorporated nomogram showed good performance (P < 0.001) for EFS estimation (C-Index: 0.810; 95% CI: 0.738–0.882), which was comparable with that of the clinicopathological model for EFS estimation (C-Index: 0.81 vs. 0.85). The radiomics-based nomogram failed to show incremental prognostic value compared with that using the clinicopathologic model. The combined model (radiomics signature plus clinicopathologic parameters) showed significant improvement in the discriminatory accuracy, along with good calibration and greater net clinical benefit, of EFS (C-Index: 0.88; 95% CI: 0.829–0.933). Conclusion: The radiomics signature can be used as a prognostic indicator for EFS in patients with hepatoblastoma. A combination of the radiomics signature and clinicopathologic risk factors showed better performance in terms of EFS prediction in patients with hepatoblastoma, which enabled precise clinical decision-making.
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Affiliation(s)
- Yi Jiang
- West China Second University Hospital, Sichuan University, Chengdu, China
| | - Jingjing Sun
- West China Second University Hospital, Sichuan University, Chengdu, China
| | - Yuwei Xia
- Huiying Medical Technology, Beijing, China
| | - Yan Cheng
- West China Second University Hospital, Sichuan University, Chengdu, China
| | - Linjun Xie
- West China Second University Hospital, Sichuan University, Chengdu, China
| | - Xia Guo
- West China Second University Hospital, Sichuan University, Chengdu, China
| | - Yingkun Guo
- West China Second University Hospital, Sichuan University, Chengdu, China
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