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Yang Y, Ji X, Li S, Gao X, Wang Y, Huang Y. Ultrasound-based radiomics for predicting the five major histological subtypes of epithelial ovarian cancer. BMC Med Imaging 2025; 25:122. [PMID: 40234786 PMCID: PMC12001453 DOI: 10.1186/s12880-025-01624-1] [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: 01/13/2024] [Accepted: 03/03/2025] [Indexed: 04/17/2025] Open
Abstract
BACKGROUND Computational approaches have been proposed using radiomics in order to assess tumour heterogeneity, which is motivated by the concept that biomedical images may contain underlying pathophysiology information and has the potential to quantitatively measure the heterogeneity of intra- and intertumours. Ovarian cancer has the highest mortality among malignant tumours of female reproductive system and can be further divided into many subtypes with different management strategies and prognosis. The purpose of our study is to develop and validate ultrasound-based radiomics models to distinguish the five major histological subtypes of epithelial ovarian cancer. METHODS From January 2018 to August 2022, 1209 eligible ovarian cancer patients were enrolled. There were two subjects in this study: all patients (n = 1209) and patients with the five major histological subtypes (n = 1039). After image segmentation manually, radiomics features were extracted and some clinical characteristics were added. Nine feature selection methods were used to select the optimal predictive features. Seven classifiers were carried out to construct models. Choose the combination with the best predictive performance as the final result. RESULTS As for low-grade serous carcinoma, endometrioid carcinoma, and clear cell carcinoma, the models yields AUCs below 0.80 in the 10-fold cross-validation in the two groups. As for mucinous carcinoma, the AUCs were 0.83(95%CI, 0.74-0.93) and 0.89(95%CI, 0.83-0.95) in the validation cohorts and 0.80(95%CI, 0.73-0.87) and 0.86(95%CI, 0.78-0.94) in the 10-fold cross-validation in the two groups, respectively. As for high-grade serous carcinoma (HGSC), the models showed AUCs of 0.87(95%CI, 0.83-0.91) and 0.85(95%CI, 0.81-0.89) in the validation cohorts and 0.87(95%CI, 0.85-0.89) and 0.84(95%CI, 0.81-0.87) in the 10-fold cross-validation in the two groups, respectively, and exhibited high consistency between the predicted results and the actual outcomes, and brought great net benefits for patients. CONCLUSIONS The ultrasound-based radiomics models in discriminating HGSC and non-HGSC showed good predictive performance, as well as high consistency between the predicted results and the actual outcomes, and brought significant net benefits for patients.
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Affiliation(s)
- Yang Yang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xinyu Ji
- Department of Ultrasound, Shengjing Hospital, China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China
| | - Sen Li
- Yizhun Medical AI, Beijing, China
| | - Xuemeng Gao
- Department of Ultrasound, Shengjing Hospital, China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China
| | - Yitong Wang
- Department of Ultrasound, Shengjing Hospital, China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China
| | - Ying Huang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- Department of Ultrasound, Shengjing Hospital, China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China.
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Moro F, Vagni M, Tran HE, Bernardini F, Mascilini F, Ciccarone F, Nero C, Giannarelli D, Boldrini L, Fagotti A, Scambia G, Valentin L, Testa AC. Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid morphology on ultrasound. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2025; 65:353-363. [PMID: 38748935 PMCID: PMC11872347 DOI: 10.1002/uog.27680] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/19/2024] [Accepted: 05/02/2024] [Indexed: 02/04/2025]
Abstract
OBJECTIVE The primary aim was to identify radiomics ultrasound features that can distinguish between benign and malignant adnexal masses with solid ultrasound morphology, and between primary malignant (including borderline and primary invasive) and metastatic solid ovarian masses, and to develop ultrasound-based machine learning models that include radiomics features to discriminate between benign and malignant solid adnexal masses. The secondary aim was to compare the discrimination performance of our newly developed radiomics models with that of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and that of subjective assessment by an experienced ultrasound examiner. METHODS This was a retrospective, observational single-center study conducted at Fondazione Policlinico Universitario A. Gemelli IRCC, in Rome, Italy. Included were patients with a histological diagnosis of an adnexal tumor with solid morphology according to International Ovarian Tumor Analysis (IOTA) terminology at preoperative ultrasound examination performed in 2014-2020, who were managed with surgery. The patient cohort was split randomly into training and validation sets at a ratio of 70:30 and with the same proportion of benign and malignant tumors in the two subsets, with malignant tumors including borderline, primary invasive and metastatic tumors. We extracted 68 radiomics features, belonging to two different families: intensity-based statistical features and textural features. Models to predict malignancy were built based on a random forest classifier, fine-tuned using 5-fold cross-validation over the training set, and tested on the held-out validation set. The variables used in model-building were patient age and radiomics features that were statistically significantly different between benign and malignant adnexal masses and assessed as not redundant based on the Pearson correlation coefficient. We evaluated the discriminative ability of the models and compared it to that of the ADNEX model and that of subjective assessment by an experienced ultrasound examiner using the area under the receiver-operating-characteristics curve (AUC) and classification performance by calculating sensitivity and specificity. RESULTS In total, 326 patients were included and 775 preoperative ultrasound images were analyzed. Of the 68 radiomics features extracted, 52 differed statistically significantly between benign and malignant tumors in the training set, and 18 uncorrelated features were selected for inclusion in model-building. The same 52 radiomics features differed significantly between benign, primary malignant and metastatic tumors. However, the values of the features manifested overlapped between primary malignant and metastatic tumors and did not differ significantly between them. In the validation set, 25/98 (25.5%) tumors were benign and 73/98 (74.5%) were malignant (6 borderline, 57 primary invasive, 10 metastatic). In the validation set, a model including only radiomics features had an AUC of 0.80, sensitivity of 0.78 and specificity of 0.76 at an optimal cut-off for risk of malignancy of 68%, based on Youden's index. The corresponding results for a model including age and radiomics features were AUC of 0.79, sensitivity of 0.86 and specificity of 0.56 (cut-off 60%, based on Youden's index), while those of the ADNEX model were AUC of 0.88, sensitivity of 0.99 and specificity of 0.64 (at a 20% risk-of-malignancy cut-off). Subjective assessment had a sensitivity of 0.99 and specificity of 0.72. CONCLUSIONS Our radiomics model had moderate discriminative ability on internal validation and the addition of age to this model did not improve its performance. Even though our radiomics models had discriminative ability inferior to that of the ADNEX model, our results are sufficiently promising to justify continued development of radiomics analysis of ultrasound images of adnexal masses. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- F. Moro
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità PubblicaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - M. Vagni
- Istituto di RadiologiaUniversità Cattolica del Sacro CuoreRomeItaly
| | - H. E. Tran
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed EmatologiaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - F. Bernardini
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità PubblicaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - F. Mascilini
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità PubblicaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - F. Ciccarone
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità PubblicaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - C. Nero
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità PubblicaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - D. Giannarelli
- Epidemiology and Biostatistics Facility, G‐STeP GeneratorFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - L. Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed EmatologiaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - A. Fagotti
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità PubblicaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Dipartimento Universitario Scienze della Vita e Sanità PubblicaUniversità Cattolica del Sacro CuoreRomeItaly
| | - G. Scambia
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità PubblicaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Dipartimento Universitario Scienze della Vita e Sanità PubblicaUniversità Cattolica del Sacro CuoreRomeItaly
| | - L. Valentin
- Department of Obstetrics and GynecologySkåne University HospitalMalmöSweden
- Department of Clinical SciencesLund UniversityMalmöSweden
| | - A. C. Testa
- Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità PubblicaFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Dipartimento Universitario Scienze della Vita e Sanità PubblicaUniversità Cattolica del Sacro CuoreRomeItaly
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Shi L, Li C, Bai Y, Cao Y, Zhao S, Chen X, Cheng Z, Zhang Y, Li H. CT radiomics to predict pathologic complete response after neoadjuvant immunotherapy plus chemoradiotherapy in locally advanced esophageal squamous cell carcinoma. Eur Radiol 2025; 35:1594-1604. [PMID: 39470794 DOI: 10.1007/s00330-024-11141-4] [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: 03/10/2024] [Revised: 08/26/2024] [Accepted: 09/19/2024] [Indexed: 11/01/2024]
Abstract
OBJECTIVE To develop and validate a CT-based radiomics model to predict pathologic complete response (pCR) after neoadjuvant immunotherapy plus chemoradiotherapy (NICRT) in locally advanced esophageal squamous cell carcinoma (ESCC). METHODS A total of 105 patients with locally advanced ESCC receiving NICRT from February 2019 to December 2023 were enrolled. Patients were randomly divided into the training cohort and the test cohort at a 3:1 ratio. Enhanced CT scans were obtained before NICRT treatment. The 2D and 3D regions of interest were segmented, and features were extracted, followed by feature selection. Six algorithms were applied to construct the radiomics and clinical models. These models were evaluated by area under curve (AUC), accuracy, sensitivity, and specificity, and their respective optimal algorithms were further compared. RESULTS Forty-eight patients (45.75%) achieved pCR after NICRT. The AUC values of three algorithms in 2D radiomics models were higher than those in the 3D radiomics model and clinical model. Among these, the 2D radiomics model based on eXtreme Gradient Boosting (XGBoost) exhibited the best performance, with an AUC of 0.89 (95% CI, 0.81-0.97), accuracy of 0.85, sensitivity of 0.86, and specificity of 0.84 in the training cohort, and an AUC of 0.80 (95% CI, 0.64-0.97), accuracy of 0.77, sensitivity of 0.84, and specificity of 0.69 in the test cohort. Calibration curves also showed good agreement between predicted and actual response, and the decision curve analysis further confirmed its clinical applicability. CONCLUSION The 2D radiomics model can effectively predict pCR to NICRT in locally advanced ESCC. KEY POINTS Question Can CT-based radiomics predict pathologic complete response (pCR) after neoadjuvant immunotherapy plus chemoradiotherapy (NICRT) in locally advanced esophageal squamous cell carcinoma (ESCC)? Findings The model based on eXtreme Gradient Boosting (XGBoost) performed best, with an AUC of 0.89 in the training and 0.80 in the test cohort. Clinical relevance This CT-based radiomics model exhibits promising performance for predicting pCR to NICRT in locally advanced ESCC, which may be valuable in personalized treatment plan optimization.
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Affiliation(s)
- Liqiang Shi
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Chengqiang Li
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yaya Bai
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yuqin Cao
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shengguang Zhao
- Department of Radiotherapy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaoyan Chen
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yajie Zhang
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Hecheng Li
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Shi SY, Li YA, Qiang JW. Multiparametric MRI-based radiomics nomogram for differentiation of primary mucinous ovarian cancer from metastatic ovarian cancer. Abdom Radiol (NY) 2025; 50:1018-1028. [PMID: 39215773 DOI: 10.1007/s00261-024-04542-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: 07/01/2024] [Revised: 08/15/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024]
Abstract
OBJECTIVE To develop a multiparametric magnetic resonance imaging (mpMRI)-based radiomics nomogram and evaluate its performance in differentiating primary mucinous ovarian cancer (PMOC) from metastatic ovarian cancer (MOC). METHODS A total of 194 patients with PMOC (n = 72) and MOC (n = 122) confirmed by histology were randomly divided into the primary cohort (n = 137) and validation cohort (n = 57). Radiomics features were extracted from axial fat-saturated T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences of each lesion. The effective features were selected by minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression to develop a radiomics model. Combined with clinical features, multivariate logistic regression analysis was employed to develop a radiomics nomogram. The efficiency of nomogram was evaluated using the receiver operating characteristic (ROC) curve analysis and compared using DeLong test. Finally, the goodness of fit and clinical benefit of nomogram were assessed by calibration curves and decision curve analysis, respectively. RESULTS The radiomics nomogram, by combining the mpMRI radiomics features with clinical features, yielded area under the curve (AUC) values of 0.931 and 0.934 in the primary and validation cohorts, respectively. The predictive performance of the radiomics nomogram was significantly superior to the radiomics model (0.931 vs. 0.870, P = 0.004; 0.934 vs. 0.844, P = 0.032), the clinical model (0.931 vs. 0.858, P = 0.005; 0.934 vs. 0.847, P = 0.030), and radiologists (all P < 0.05) in the primary and validation cohorts, respectively. The decision curve analysis revealed that the nomogram could provide higher net benefit to patients. CONCLUSION The mpMRI-based radiomics nomogram exhibited notable predictive performance in differentiating PMOC from MOC, emerging as a non-invasive preoperative imaging approach.
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Affiliation(s)
- Shu Yi Shi
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yong Ai Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Changzhi People's Hospital, Changzhi, Shanxi, China
| | - Jin Wei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
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Tsili AC. Updates on Imaging of Common Urogenital Neoplasms. Cancers (Basel) 2024; 17:84. [PMID: 39796713 PMCID: PMC11719912 DOI: 10.3390/cancers17010084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2024] [Accepted: 12/24/2024] [Indexed: 01/13/2025] Open
Abstract
Urogenital neoplasms represent some of the most common malignancies [...].
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Affiliation(s)
- Athina C Tsili
- Department of Clinical Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
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Zeng S, Wang XL, Yang H. Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer. Mil Med Res 2024; 11:77. [PMID: 39673071 PMCID: PMC11645790 DOI: 10.1186/s40779-024-00580-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 11/07/2024] [Indexed: 12/15/2024] Open
Abstract
Ovarian cancer (OC) remains one of the most lethal gynecological malignancies globally. Despite the implementation of various medical imaging approaches for OC screening, achieving accurate differential diagnosis of ovarian tumors continues to pose significant challenges due to variability in image performance, resulting in a lack of objectivity that relies heavily on the expertise of medical professionals. This challenge can be addressed through the emergence and advancement of radiomics, which enables high-throughput extraction of valuable information from conventional medical images. Furthermore, radiomics can integrate with genomics, a novel approach termed radiogenomics, which allows for a more comprehensive, precise, and personalized assessment of tumor biological features. In this review, we present an extensive overview of the application of radiomics and radiogenomics in diagnosing and predicting ovarian tumors. The findings indicate that artificial intelligence methods based on imaging can accurately differentiate between benign and malignant ovarian tumors, as well as classify their subtypes. Moreover, these methods are effective in forecasting survival rates, treatment outcomes, metastasis risk, and recurrence for patients with OC. It is anticipated that these advancements will function as decision-support tools for managing OC while contributing to the advancement of precision medicine.
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Affiliation(s)
- Song Zeng
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, 110004, China
| | - Xin-Lu Wang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, 110004, China
| | - Hua Yang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, 110004, China.
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Hong Y, Peng J, Zeng Y, Deng X, Xu W, Wang J. The value of combined MRI, enhanced CT and 18F-FDG PET/CT in the diagnosis of recurrence and metastasis after surgery for ovarian cancer. Clin Transl Oncol 2024; 26:3013-3019. [PMID: 38780807 DOI: 10.1007/s12094-024-03499-0] [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: 02/17/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE The purpose of this article was to investigate the value of combined MRI, enhanced CT and 18F-FDG PET/CT in the diagnosis of recurrence and metastasis after surgery for ovarian cancer. METHODS Ninety-five ovarian cancer patients were selected as the study subjects, all of them underwent surgical treatment, and MRI, enhanced CT and 18F-FDG PET/CT were performed on all of them in the postoperative follow-up, and the pathological results after the second operation were used as the diagnostic "gold standard". The diagnostic value (sensitivity, specificity, accuracy, negative predictive value and positive predictive value) of the three examination methods alone or in combination for the diagnosis of postoperative recurrence and metastasis of ovarian cancer was compared, and the detection rate was calculated when the lesion was the unit of study, so as to compare the efficacy of the three methods in the diagnosis of postoperative recurrent metastatic lesions of ovarian cancer. RESULTS The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the combined group were higher than those of MRI and enhanced CT for recurrence and metastasis of ovarian cancer after surgery, and the specificity, accuracy and positive predictive value of the combined group were higher than those of the 18F-FDG PET/CT group, and those of the 18F-FDG PET/CT group were higher than those of the enhanced CT group (all P < 0.05). When the postoperative recurrent metastatic lesions of ovarian cancer were used as the study unit, the detection rate of lesions in the combined group was higher than that of the three examinations detected individually, and the detection rate of lesions in 18F-FDG PET/CT was higher than that of enhanced CT and MRI (P < 0.05). CONCLUSION The combination of MRI, enhanced CT and 18F-FDG PET/CT can accurately diagnose recurrence and metastasis of ovarian cancer after surgery, detect recurrent metastatic lesions as early as possible, and improve patients' prognosis.
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Affiliation(s)
- Yong Hong
- Department of Radiology, Huadu District People's Hospital of Guangzhou, 48 Xinhua Road, Huadu District, Guangzhou, 510800, China.
| | - Jianfeng Peng
- Department of Radiology, Huadu District People's Hospital of Guangzhou, 48 Xinhua Road, Huadu District, Guangzhou, 510800, China
| | - Yanni Zeng
- Department of Radiology, Huadu District People's Hospital of Guangzhou, 48 Xinhua Road, Huadu District, Guangzhou, 510800, China
| | - Xuewen Deng
- Department of Radiology, Huadu District People's Hospital of Guangzhou, 48 Xinhua Road, Huadu District, Guangzhou, 510800, China
| | - Wanjun Xu
- Department of Radiology, Huadu District People's Hospital of Guangzhou, 48 Xinhua Road, Huadu District, Guangzhou, 510800, China
| | - Juanting Wang
- Department of Radiology, Huadu District People's Hospital of Guangzhou, 48 Xinhua Road, Huadu District, Guangzhou, 510800, China
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Lan W, Hong J, Huayun T. Advances in ovarian cancer radiomics: a bibliometric analysis from 2010 to 2024. Front Oncol 2024; 14:1456932. [PMID: 39411123 PMCID: PMC11473287 DOI: 10.3389/fonc.2024.1456932] [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: 06/29/2024] [Accepted: 09/09/2024] [Indexed: 10/19/2024] Open
Abstract
Objective Ovarian cancer, a leading cause of death among gynecological malignancies, often eludes early detection, leading to diagnoses at advanced stages. The objective of this bibliometric analysis is to map the landscape of ovarian cancer radiomics research from 2010 to 2024, emphasizing its growth, global contributions, and the impact of emerging technologies on early diagnosis and treatment strategies. Methods A comprehensive search was conducted using the Web of Science Core Collection (WoSCC), focusing on publications related to radiomics and ovarian cancer within the specified period. Analytical tools such as VOSviewer and CiteSpace were employed to visualize trends, collaborations, and key contributions, while the R programming environment offered further statistical insights. Results From the initial dataset, 149 articles were selected, showing a significant increase in research output, especially in the years 2021-2023. The analysis revealed a dominant contribution from China, with significant inputs from England. Major institutional contributors included the University of Cambridge and GE Healthcare. 'Frontiers in Oncology' emerged as a crucial journal in the field, according to Bradford's Law. Keyword analysis highlighted the focus on advanced imaging techniques and machine learning. Conclusions The steady growth in ovarian cancer radiomics research reflects its critical role in advancing diagnostic and prognostic methodologies, underscoring the potential of radiomics in the shift towards personalized medicine. Despite some methodological challenges, the field's dynamic evolution suggests a promising future for radiomics in enhancing the accuracy of ovarian cancer diagnosis and treatment, contributing to improved patient care and outcomes.
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Affiliation(s)
| | | | - Tan Huayun
- Department of Obstetrics, Weifang People's Hospital, Shandong Second Medical University, Weifang, China
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Zhang X, Wu S, Zu X, Li X, Zhang Q, Ren Y, Qian X, Tong S, Li H. Ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer. Front Oncol 2024; 14:1438923. [PMID: 39359429 PMCID: PMC11445231 DOI: 10.3389/fonc.2024.1438923] [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: 05/27/2024] [Accepted: 08/29/2024] [Indexed: 10/04/2024] Open
Abstract
Purpose Accurate preoperative identification of Human epidermal growth factor receptor 2 (HER2) low expression breast cancer (BC) is critical for clinical decision-making. Our aim was to use machine learning methods to develop and validate an ultrasound-based radiomics nomogram for predicting HER2-low expression in BC. Methods In this retrospective study, 222 patients (108 HER2-0 expression and 114 HER2-low expression) with BC were included. The enrolled patients were randomly divided into a training cohort and a test cohort with a ratio of 8:2. The tumor region of interest was manually delineated from ultrasound image, and radiomics features were subsequently extracted. The features underwent dimension reduction using the least absolute shrinkage and selection operator (LASSO) algorithm, and rad-score were calculated. Five machine learning algorithms were applied for training, and the algorithm demonstrating the best performance was selected to construct a radiomics (USR) model. Clinical risk factors were integrated with rad-score to construct the prediction model, and a nomogram was plotted. The performance of the nomogram was assessed using receiver operating characteristic curve and decision curve analysis. Results A total of 480 radiomics features were extracted, out of which 11 were screened out. The majority of the extracted features were wavelet features. Subsequently, the USR model was established, and rad-scores were computed. The nomogram, incorporating rad-score, tumor shape, border, and microcalcification, achieved the best performance in both the training cohort (AUC 0.89; 95%CI 0.836-0.936) and the test cohort (AUC 0.84; 95%CI 0.722-0.958), outperforming both the USR model and clinical model. The calibration curves showed satisfactory consistency, and DCA confirmed the clinical utility of the nomogram. Conclusion The nomogram model based on ultrasound radiomics exhibited high prediction value for HER2-low BC.
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Affiliation(s)
- Xueling Zhang
- Department of Ultrasound Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Shaoyou Wu
- Materdicine Lab, School of Life Sciences, Shanghai University, Shanghai, China
| | - Xiao Zu
- Department of Ultrasound Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xiaojing Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Qing Zhang
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Yongzhen Ren
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Xiaoqin Qian
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Shan Tong
- Department of Ultrasound Medicine, Jiangsu University Affiliated People’s Hospital, Zhenjiang, China
| | - Hongbo Li
- Department of Ultrasound Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Ultrasound Medicine, People’s Hospital of Longhua, Shenzhen, China
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Takeyama N, Sasaki Y, Ueda Y, Tashiro Y, Tanaka E, Nagai K, Morioka M, Ogawa T, Tate G, Hashimoto T, Ohgiya Y. Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study. Jpn J Radiol 2024; 42:731-743. [PMID: 38472624 PMCID: PMC11217043 DOI: 10.1007/s11604-024-01545-z] [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: 10/23/2023] [Accepted: 02/02/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE To retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC). MATERIALS AND METHODS Thirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation method was performed to select features. Logistic regression analysis was conducted to construct the discriminating models. Receiver operating characteristic curve (ROC) analyses were performed to predict CCC. RESULTS Four features with the highest absolute value of the LASSO algorithm were selected for the MRI-based, RA-based, and combined models: the ADC value, absence of thickening of the uterine endometrium, absence of peritoneal dissemination, and growth pattern of the solid component for the MRI-based model; Gray-Level Run Length Matrix (GLRLM) Long Run Low Gray-Level Emphasis (LRLGLE) on T2WI, spherical disproportion and Gray-Level Size Zone Matrix (GLSZM), Large Zone High Gray-Level Emphasis (LZHGE) on cT1WI, and GLSZM Normalized Gray-Level Nonuniformity (NGLN) on ADC map for the RA-based model; and the ADC value, spherical disproportion and GLSZM_LZHGE on cT1WI, and GLSZM_NGLN on ADC map for the combined model. Area under the ROC curves of those models were 0.895, 0.910, and 0.956. The diagnostic performance of the combined model was significantly superior (p = 0.02) to that of the MRI-based model. No significant differences were observed between the combined and RA-based models. CONCLUSION Conventional MRI-based analysis can effectively distinguish CCC from EC. The combination of RA-based features with MRI-based features may assist in differentiating between the two diseases.
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Affiliation(s)
- Nobuyuki Takeyama
- Department of Radiology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo, 142-8666, Japan.
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan.
| | - Yasushi Sasaki
- Department of Obstetrics and Gynecology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, Kanagawa, 227-8501, Japan
| | - Yasuo Ueda
- Department of Pathology and Laboratory Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Yuki Tashiro
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Eliko Tanaka
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
- Department of Radiology, Kawasaki Saiwai Hospital, 31-27 Ohmiya-Tyo, Saiwai-Ku, Kawasaki City, Kanagawa, 212-0014, Japan
| | - Kyoko Nagai
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Miki Morioka
- Department of Obstetrics and Gynecology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, Kanagawa, 227-8501, Japan
| | - Takafumi Ogawa
- Department of Pathology and Laboratory Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Genshu Tate
- Department of Pathology and Laboratory Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Toshi Hashimoto
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Yoshimitsu Ohgiya
- Department of Radiology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo, 142-8666, Japan
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Tsili AC, Alexiou G, Tzoumpa M, Siempis T, Argyropoulou MI. Imaging of Peritoneal Metastases in Ovarian Cancer Using MDCT, MRI, and FDG PET/CT: A Systematic Review and Meta-Analysis. Cancers (Basel) 2024; 16:1467. [PMID: 38672549 PMCID: PMC11048266 DOI: 10.3390/cancers16081467] [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: 02/07/2024] [Revised: 04/05/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
This review aims to compare the diagnostic performance of multidetector CT (MDCT), MRI, including diffusion-weighted imaging, and FDG PET/CT in the detection of peritoneal metastases (PMs) in ovarian cancer (OC). A comprehensive search was performed for articles published from 2000 to February 2023. The inclusion criteria were the following: diagnosis/suspicion of PMs in patients with ovarian/fallopian/primary peritoneal cancer; initial staging or suspicion of recurrence; MDCT, MRI and/or FDG PET/CT performed for the detection of PMs; population of at least 10 patients; surgical results, histopathologic analysis, and/or radiologic follow-up, used as reference standard; and per-patient and per-region data and data for calculating sensitivity and specificity reported. In total, 33 studies were assessed, including 487 women with OC and PMs. On a per-patient basis, MRI (p = 0.03) and FDG PET/CT (p < 0.01) had higher sensitivity compared to MDCT. MRI and PET/CT had comparable sensitivities (p = 0.84). On a per-lesion analysis, no differences in sensitivity estimates were noted between MDCT and MRI (p = 0.25), MDCT and FDG PET/CT (p = 0.68), and MRI and FDG PET/CT (p = 0.35). Based on our results, FDG PET/CT and MRI are the preferred imaging modalities for the detection of PMs in OC. However, the value of FDG PET/CT and MRI compared to MDCT needs to be determined. Future research to address the limitations of the existing studies and the need for standardization and to explore the cost-effectiveness of the three imaging modalities is required.
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Affiliation(s)
- Athina C. Tsili
- Department of Clinical Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, University Campus, 45110 Ioannina, Greece; (M.T.); (M.I.A.)
| | - George Alexiou
- Department of Neurosurgery, Faculty of Medicine, School of Health Sciences, University of Ioannina, University Campus, 45110 Ioannina, Greece;
| | - Martha Tzoumpa
- Department of Clinical Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, University Campus, 45110 Ioannina, Greece; (M.T.); (M.I.A.)
| | - Timoleon Siempis
- ENT Department, Ulster Hospital, Upper Newtownards Rd., Dundonald, Belfast BT16 1RH, UK;
| | - Maria I. Argyropoulou
- Department of Clinical Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, University Campus, 45110 Ioannina, Greece; (M.T.); (M.I.A.)
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Na I, Noh JJ, Kim CK, Lee JW, Park H. Combined radiomics-clinical model to predict platinum-sensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI. Front Oncol 2024; 14:1341228. [PMID: 38327741 PMCID: PMC10847571 DOI: 10.3389/fonc.2024.1341228] [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: 11/20/2023] [Accepted: 01/05/2024] [Indexed: 02/09/2024] Open
Abstract
Introduction We aimed to predict platinum sensitivity using routine baseline multimodal magnetic resonance imaging (MRI) and established clinical data in a radiomics framework. Methods We evaluated 96 patients with ovarian cancer who underwent multimodal MRI and routine laboratory tests between January 2016 and December 2020. The patients underwent diffusion-weighted, contrast-enhanced T1-weighted, and T2-weighted MRI. Subsequently, 293 radiomic features were extracted by manually identifying tumor regions of interest. The features were subjected to the least absolute shrinkage and selection operators, leaving only a few selected features. We built the first prediction model with a tree-based classifier using selected radiomics features. A second prediction model was built by combining the selected radiomic features with four established clinical factors: age, disease stage, initial tumor marker level, and treatment course. Both models were built and tested using a five-fold cross-validation. Results Our radiomics model predicted platinum sensitivity with an AUC of 0.65 using a few radiomics features related to heterogeneity. The second combined model had an AUC of 0.77, confirming the incremental benefits of the radiomics model in addition to models using established clinical factors. Conclusion Our combined radiomics-clinical data model was effective in predicting platinum sensitivity in patients with advanced ovarian cancer.
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Affiliation(s)
- Inye Na
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Joseph J. Noh
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeong-Won Lee
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
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Chen J, Yang F, Liu C, Pan X, He Z, Fu D, Jin G, Su D. Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors. Eur J Med Res 2023; 28:609. [PMID: 38115095 PMCID: PMC10729460 DOI: 10.1186/s40001-023-01561-1] [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: 09/03/2022] [Accepted: 11/30/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND This study aimed to identify the diagnostic value of models constructed using computed tomography-based radiomics features for discrimination of benign and early stage malignant ovarian tumors. METHODS The imaging and clinicopathological data of 197 cases of benign and early stage malignant ovarian tumors (FIGO stage I/II), were retrospectively analyzed. The patients were randomly assigned into training data set and validation data set. Radiomics features were extracted from images of plain computed tomography scan and contrast-enhanced computed tomography scan, were then screened in the training data set, and a radiomics model was constructed. Multivariate logistic regression analysis was used to construct a radiomic nomogram, containing the traditional diagnostic model and the radiomics model. Moreover, the decision curve analysis was used to assess the clinical application value of the radiomics nomogram. RESULTS Six textural features with the greatest diagnostic efficiency were finally screened. The value of the area under the receiver operating characteristic curve showed that the radiomics nomogram was superior to the traditional diagnostic model and the radiomics model (P < 0.05) in the training data set. In the validation data set, the radiomics nomogram was superior to the traditional diagnostic model (P < 0.05), but there was no statistically significant difference compared to the radiomics model (P > 0.05). The calibration curve and the Hosmer-Lemeshow test revealed that the three models all had a great degree of fit (All P > 0.05). The results of decision curve analysis indicated that utilization of the radiomics nomogram to distinguish benign and early stage malignant ovarian tumors had a greater clinical application value when the risk threshold was 0.4-1.0. CONCLUSIONS The computed tomography-based radiomics nomogram could be a non-invasive and reliable imaging method to discriminate benign and early stage malignant ovarian tumors.
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Affiliation(s)
- Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Fei Yang
- Department of Clinical Medical, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, People's Republic of China
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Xinwei Pan
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Danhui Fu
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Guanqiao Jin
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
| | - Danke Su
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
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14
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Sadowski EA, Lees B, McMillian AB, Kusmirek JE, Cho SY, Barroilhet LM. Distribution of prostate specific membrane antigen (PSMA) on PET-MRI in patients with and without ovarian cancer. Abdom Radiol (NY) 2023; 48:3643-3652. [PMID: 37261441 DOI: 10.1007/s00261-023-03957-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/09/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023]
Abstract
OBJECTIVES Ovarian cancer is the most lethal cancer and future research needs to focus on the early detection and exploration of new therapeutic agents. The objectives of this proof-of-concept study are to assess the feasibility of PSMA 18F-DCFPyl PET/MR imaging for detecting ovarian cancer and to evaluate the PSMA distribution in patients with and without ovarian cancer. METHODS This prospective pilot proof-of-concept study in patients with and without ovarian cancers occurred between October 2017 and January 2020. Patients were recruited from gynecologic oncology or hereditary ovarian cancer clinics, and underwent surgical removal of the uterus and ovaries for gynecologic indications. PSMA 18F-DCFPyl PET/MRI was obtained prior to standard of care surgery. RESULTS Fourteen patients were scanned: four patients with normal ovaries, six patients with benign ovarian lesions, and four patients with malignant ovarian lesions. Tracer uptake in normal ovaries (SUVmax = 2.8 ± 0.4) was greater than blood pool (SUVmax = 1.8 ± 0.5, p < 0.0001). Tracer uptake in benign ovarian lesions (2.2 ± 1.0) did not differ significantly from blood pool (p = 0.331). Tracer uptake in ovarian cancer (SUVmax = 7.8 ± 3.8) was greater than blood pool (p < 0.0001), normal ovaries (p = 0.0014), and benign ovarian lesions (p = 0.005). CONCLUSION PET/MR imaging detected PSMA uptake in ovarian cancer, with little to no uptake in benign ovarian findings. These results are encouraging and further studies in a larger patient cohort would be useful to help determine the extent and heterogeneity of PSMA uptake in ovarian cancer patients.
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Affiliation(s)
- Elizabeth A Sadowski
- Departments of Radiology, Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, E3/372, Madison, WI, 53792-3252, USA.
| | - Brittany Lees
- Atrium Health Levine Cancer Institute, 1021 Morehead Medical Drive, Suite 2100, Charlotte, NC, 28204, USA
| | - Alan B McMillian
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 1111 Highland Avenue, Rm 1139, Madison, WI, 53705, USA
| | - Joanna E Kusmirek
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., E3/372, Madison, WI, 53792-3252, USA
| | - Steve Y Cho
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., E3/372, Madison, WI, 53792-3252, USA
| | - Lisa M Barroilhet
- Departments of Radiology, Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, E3/372, Madison, WI, 53792-3252, USA
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15
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Chen J, Xu K, Li C, Tian Y, Li L, Wen B, He C, Cai H, He Y. [ 68Ga]Ga-FAPI-04 PET/CT in the evaluation of epithelial ovarian cancer: comparison with [ 18F]F-FDG PET/CT. Eur J Nucl Med Mol Imaging 2023; 50:4064-4076. [PMID: 37526694 DOI: 10.1007/s00259-023-06369-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023]
Abstract
PURPOSE To compare the efficacy of [68Ga]Ga-FAPI-04 PET/CT in primary or recurrent tumors and metastatic lesions of epithelial ovarian cancer (EOC) with that of fluorine-18 fluorodeoxyglucose ([18F]F-FDG) PET/CT. METHODS Forty-nine patients (median age, 57 years; IQR, 51-66 years) with histologically proven primary or relapsed EOC were enrolled. Participants underwent [18F]F-FDG and [68Ga]Ga-FAPI-04 PET/CT. The detection rate, diagnostic accuracy, semiquantitative parameters, tumor staging, and clinical management of the tracers were compared. The diagnostic performance of [18F]F-FDG and [68Ga]Ga-FAPI-04 PET/CT was evaluated and compared using surgical pathology. Differences between methods regarding the peritoneal cancer index (PCI) using preoperative imaging, surgical PCI, and tumor markers (CA125, HE4) were also assessed regarding peritoneal metastases. RESULTS Among the 49 patients, 28 had primary EOC; 21 had relapsed EOC. [68Ga]Ga-FAPI-04 PET/CT outperformed [18F]F-FDG PET/CT in detecting peritoneal metastases (96.8% vs. 83.0%; p < 0.001), retroperitoneal (99.5% vs. 91.4%; p < 0.001), and supradiaphragmatic lymph node metastases (100% vs. 80.4%; p < 0.001). Compared with [18F]F-FDG, [68Ga]Ga-FAPI-04 showed higher SUVmax for peritoneal metastases (17.31 vs. 13.68; p = 0.026) and retroperitoneal (8.72 vs. 6.56; p < 0.001) and supradiaphragmatic lymph node metastases (6.39 vs. 4.20; p < 0.001). Moreover, [68Ga]Ga-FAPI-04 PET/CT showed higher sensitivity compared with [18F]F-FDG PET/CT for detecting metastatic lymph nodes (80.6% vs. 61.3%; p = 0.031) and peritoneal metastases (97.5% vs. 75.9%; p < 0.001), using surgical pathology as the gold standard. Compared with [18F]F-FDG PET/CT, [68Ga]Ga-FAPI-04 PET/CT led to an upgrade in 14.3% and 33.3% of treatment-naive and relapse participants, resulting in management changes in 10.7% and 19.0% of the patients, respectively. The median PCIFAPI scores were significantly higher than PCIFDG (15 vs. 11; p < 0.001) and positively correlated with CA125 and HE4 levels and surgical PCI. CONCLUSION [68Ga]Ga-FAPI-04 PET/CT achieved higher sensitivity than [18F]F-FDG PET/CT in the detection and diagnosis of lymph node and peritoneal metastases, suggesting advantages regarding the preoperative staging of patients with EOC and, thereby, improving treatment decision-making. TRIAL REGISTRATION NCT05034146. Registered February 23, 2021.
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Affiliation(s)
- Jie Chen
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, China
| | - Kui Xu
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, China
| | - Chongjiao Li
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, China
| | - Yueli Tian
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, China
| | - Ling Li
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, China
| | - Bing Wen
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, China
| | - Can He
- Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, China
| | - Hongbing Cai
- Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, China.
| | - Yong He
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, China.
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Xu W, Zhu C, Ji D, Qian H, Shi L, Mao X, Zhou H, Wang L. CT-based radiomics prediction of CXCL13 expression in ovarian cancer. Med Phys 2023; 50:6801-6814. [PMID: 37690459 DOI: 10.1002/mp.16730] [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: 12/30/2022] [Revised: 06/05/2023] [Accepted: 08/08/2023] [Indexed: 09/12/2023] Open
Abstract
BACKGROUND Ovarian cancer, the most common malignancy in the female reproductive system, and patients tend to be at middle and advanced clinical stages when diagnosed. Therefore, early detection and early diagnosis have important clinical significance for the treatment of ovarian cancer patients. CXCL13, a chemokine with the ligands CXCR3 and CXCR5, is involved in the tumor metastasis process. PURPOSE This study aimed to predict mRNA expression of CXCL13 in ovarian cancer tissues noninvasively. METHODS Medical imaging data and transcriptomic sequencing data of the 343 ovarian cancer patients were downloaded from the TCIA and TCGA databases, respectively. Seventy-six radiomics features were extracted from the CT data. Seven features were selected for model construction by using logistic regression. Accuracy, specificity, sensitivity, positive predictive value, and negative predictive value were used to evaluate the radiomics model. RESULTS High CXCL13 expression was found to be a significant protective factor for OS [HR (95% CI) = 0.755 (0.622-0.916), p = 0.004]. There was a significant positive correlation between CXCL13 and the degree of eosinophil infiltration. A calibration curve and the Hosmer-Lemeshow goodness-of-fit test showed that the prediction probability of the radiomics prediction model for high expression of CXCL13 was consistent with the true value. The AUC value of the nomogram model's ability to predict OS (12 months) was 0.758. The calibration plot and DCA both showed high clinical applicability for the nomogram model. CONCLUSION CXCL13 is a candidate predictive biomarker for OC and correlates with the degree of plasma cell and eosinophil infiltration.
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Affiliation(s)
- Wenting Xu
- Department of Reproduction, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China
| | - Chengyi Zhu
- Department of Reproduction, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China
| | - Dan Ji
- X-ray Department, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China
| | - Haiqing Qian
- Department of Reproduction, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China
| | - Lingli Shi
- Department of Reproduction, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China
| | - Xuping Mao
- X-ray Department, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China
| | - Huifang Zhou
- Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Lihong Wang
- Department of Reproduction, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China
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Deeparani M, Kalamani M. Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer. Diagnostics (Basel) 2023; 13:3131. [PMID: 37835875 PMCID: PMC10572945 DOI: 10.3390/diagnostics13193131] [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: 06/24/2023] [Revised: 09/01/2023] [Accepted: 09/04/2023] [Indexed: 10/15/2023] Open
Abstract
Accurate and early detection of malignant pelvic mass is important for a suitable referral, triage, and for further care for the women diagnosed with a pelvic mass. Several deep learning (DL) methods have been proposed to detect pelvic masses but other methods cannot provide sufficient accuracy and increase the computational time while classifying the pelvic mass. To overcome these issues, in this manuscript, the evolutionary gravitational neocognitron neural network optimized with nomadic people optimizer for gynecological abdominal pelvic masses classification is proposed for classifying the pelvic masses (EGNNN-NPOA-PM-UI). The real time ultrasound pelvic mass images are augmented using random transformation. Then the augmented images are given to the 3D Tsallis entropy-based multilevel thresholding technique for extraction of the ROI region and its features are further extracted with the help of fast discrete curvelet transform with the wrapping (FDCT-WRP) method. Therefore, in this work, EGNNN optimized with nomadic people optimizer (NPOA) was utilized for classifying the gynecological abdominal pelvic masses. It was executed in PYTHON and the efficiency of the proposed method analyzed under several performance metrics. The proposed EGNNN-NPOA-PM-UI methods attained 99.8%. Ultrasound image analysis using the proposed EGNNN-NPOA-PM-UI methods can accurately predict pelvic masses analyzed with the existing methods.
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Affiliation(s)
- M. Deeparani
- Department of Biomedical Engineering, Hindusthan College of Engineering and Technology, Coimbatore 641032, India;
| | - M. Kalamani
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, India
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Zhan F, He L, Yu Y, Chen Q, Guo Y, Wang L. A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer. Sci Rep 2023; 13:16397. [PMID: 37773310 PMCID: PMC10541909 DOI: 10.1038/s41598-023-43543-7] [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: 05/25/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023] Open
Abstract
We developed and validated a multimodal radiomic machine learning approach to noninvasively predict the expression of lymphocyte cell-specific protein-tyrosine kinase (LCK) expression and clinical prognosis of patients with high-grade serous ovarian cancer (HGSOC). We analyzed gene enrichment using 343 HGSOC cases extracted from The Cancer Genome Atlas. The corresponding biomedical computed tomography images accessed from The Cancer Imaging Archive were used to construct the radiomic signature (Radscore). A radiomic nomogram was built by combining the Radscore and clinical and genetic information based on multimodal analysis. We compared the model performances and clinical practicability via area under the curve (AUC), Kaplan-Meier survival, and decision curve analyses. LCK mRNA expression was associated with the prognosis of HGSOC patients, serving as a significant prognostic marker of the immune response and immune cells infiltration. Six radiomic characteristics were chosen to predict the expression of LCK and overall survival (OS) in HGSOC patients. The logistic regression (LR) radiomic model exhibited slightly better predictive abilities than the support vector machine model, as assessed by comparing combined results. The performance of the LR radiomic model for predicting the level of LCK expression with five-fold cross-validation achieved AUCs of 0.879 and 0.834, respectively, in the training and validation sets. Decision curve analysis at 60 months demonstrated the high clinical utility of our model within thresholds of 0.25 and 0.7. The radiomic nomograms were robust and displayed effective calibration. Abnormally high expression of LCK in HGSOC patients is significantly correlated with the tumor immune microenvironment and can be used as an essential indicator for predicting the prognosis of HGSOC. The multimodal radiomic machine learning approach can capture the heterogeneity of HGSOC, noninvasively predict the expression of LCK, and replace LCK for predictive analysis, providing a new idea for predicting the clinical prognosis of HGSOC and formulating a personalized treatment plan.
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Affiliation(s)
- Feng Zhan
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China
- College of Engineering, Fujian Jiangxia University, Fuzhou, Fujian, People's Republic of China
| | - Lidan He
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Yuanlin Yu
- Department of Medical Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Qian Chen
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China
| | - Yina Guo
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China.
| | - Lili Wang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China
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Huang ML, Ren J, Jin ZY, Liu XY, He YL, Li Y, Xue HD. A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility. Insights Imaging 2023; 14:117. [PMID: 37395888 DOI: 10.1186/s13244-023-01464-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/11/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVES We aimed to present the state of the art of CT- and MRI-based radiomics in the context of ovarian cancer (OC), with a focus on the methodological quality of these studies and the clinical utility of these proposed radiomics models. METHODS Original articles investigating radiomics in OC published in PubMed, Embase, Web of Science, and the Cochrane Library between January 1, 2002, and January 6, 2023, were extracted. The methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses were performed to compare the methodological quality, baseline information, and performance metrics. Additional meta-analyses of studies exploring differential diagnoses and prognostic prediction in patients with OC were performed separately. RESULTS Fifty-seven studies encompassing 11,693 patients were included. The mean RQS was 30.7% (range - 4 to 22); less than 25% of studies had a high risk of bias and applicability concerns in each domain of QUADAS-2. A high RQS was significantly associated with a low QUADAS-2 risk and recent publication year. Significantly higher performance metrics were observed in studies examining differential diagnosis; 16 such studies as well as 13 exploring prognostic prediction were included in a separate meta-analysis, which revealed diagnostic odds ratios of 25.76 (95% confidence interval (CI) 13.50-49.13) and 12.55 (95% CI 8.38-18.77), respectively. CONCLUSION Current evidence suggests that the methodological quality of OC-related radiomics studies is unsatisfactory. Radiomics analysis based on CT and MRI showed promising results in terms of differential diagnosis and prognostic prediction. CRITICAL RELEVANCE STATEMENT Radiomics analysis has potential clinical utility; however, shortcomings persist in existing studies in terms of reproducibility. We suggest that future radiomics studies should be more standardized to better bridge the gap between concepts and clinical applications.
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Affiliation(s)
- Meng-Lin Huang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
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20
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Alizzi Z, Gogbashian A, Karteris E, Hall M. Development of a dual energy CT based model to assess response to treatment in patients with high grade serous ovarian cancer: a pilot cohort study. Cancer Imaging 2023; 23:62. [PMID: 37322564 DOI: 10.1186/s40644-023-00579-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/29/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND In patients with cancer, the current gold standard for assessing response to treatment involves measuring cancer lesions on computed tomography (CT) imaging. The percentage change in size of specific lesions determines whether patients have had a complete/partial response or progressive disease, according to RECIST criteria. Dual Energy CT (DECT) permits additional measurements of iodine concentration, a surrogate marker of vascularity. Here we explore the role of changes in iodine concentration within cancer tissue on CT scans to assess its suitability for determining treatment response in patients with high grade serous ovarian cancer (HGSOC). METHODS Suitable RECIST measurable lesions were identified from the CT images of HGSOC patients, taken at 2 different time points (pre and post treatment). Changes in size and iodine concentration were measured for each lesion. PR/SD were classified as responders, PD was classified as non-responder. Radiological responses were correlated with clinical and CA125 outcomes. RESULTS 62 patients had appropriate imaging for assessment. 22 were excluded as they only had one DECT scan. 32/40 patients assessed (113 lesions) had received treatment for relapsed HGSOC. RECIST and GCIG (Gynaecologic Cancer Inter Group) CA125 criteria / clinical assessment of response for patients was correlated with changes in iodine concentration, before and after treatment. The prediction of median progression free survival was significantly better associated with changes in iodine concentration (p = 0.0001) and GCIG Ca125 / clinical assessment (p = 0.0028) in comparison to RECIST criteria (p = 0.43). CONCLUSION Changes in iodine concentration from dual energy CT imaging may be more suitable than RECIST in assessing response to treatment in patients with HGSOC. TRIAL REGISTRATION CICATRIx IRAS number 198179, 14 Dec 2015, https://www.myresearchproject.org.uk/ .
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Affiliation(s)
- Zena Alizzi
- Mount Vernon Cancer Centre, Rickmansworth Road, HA6 2RN, Northwood Middx, England
- Brunel University London, Kingston Lane, UB3 8PH, Uxbridge, England
| | - Andrew Gogbashian
- Paul Strickland Scanner Centre, Rickmansworth Road, HA6 2RN, Northwood, Middlesex, England
| | | | - Marcia Hall
- Mount Vernon Cancer Centre, Rickmansworth Road, HA6 2RN, Northwood Middx, England.
- Brunel University London, Kingston Lane, UB3 8PH, Uxbridge, England.
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21
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Behr GG, Morani AC, Artunduaga M, Desoky SM, Epelman M, Friedman J, Lala SV, Seekins J, Towbin AJ, Back SJ. Imaging of pediatric ovarian tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper. Pediatr Blood Cancer 2023; 70 Suppl 4:e29995. [PMID: 36184758 PMCID: PMC10642215 DOI: 10.1002/pbc.29995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 08/26/2022] [Indexed: 11/05/2022]
Abstract
Ovarian tumors in children are uncommon. Like those arising in the adult population, they may be broadly divided into germ cell, sex cord, and surface epithelium subtypes; however, germ cell tumors comprise the majority of lesions in children, whereas tumors of surface epithelial origin predominate in adults. Diagnostic workup, including the use of imaging, requires an approach that often differs from that required in an adult. This paper offers consensus recommendations for imaging of pediatric patients with a known or suspected primary ovarian malignancy at diagnosis and during follow-up.
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Affiliation(s)
- Gerald G Behr
- Department of Radiology, Memorial Sloan Kettering Cancer Center/Weill Cornell Medicine, New York, New York, USA
| | - Ajaykumar C Morani
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Maddy Artunduaga
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Sarah M Desoky
- Department of Radiology, University of Arizona College of Medicine, Phoenix, Arizona, USA
| | - Monica Epelman
- Department of Radiology, Nicklaus Children's Hospital, Miami, Florida, USA
| | - Jonathan Friedman
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Shailee V Lala
- Department of Radiology, New York University Langone Health, New York, New York, USA
| | - Jayne Seekins
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, California, USA
| | - Alexander J Towbin
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital, Cincinnati, Ohio, USA
| | - Susan J Back
- Department of Radiology, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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22
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Panico C, Avesani G, Zormpas-Petridis K, Rundo L, Nero C, Sala E. Radiomics and Radiogenomics of Ovarian Cancer. Radiol Clin North Am 2023; 61:749-760. [PMID: 37169435 DOI: 10.1016/j.rcl.2023.02.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Ovarian cancer, one of the deadliest gynecologic malignancies, is characterized by high intra- and inter-site genomic and phenotypic heterogeneity. The traditional information provided by the conventional interpretation of diagnostic imaging studies cannot adequately represent this heterogeneity. Radiomics analyses can capture the complex patterns related to the microstructure of the tissues and provide quantitative information about them. This review outlines how radiomics and its integration with other quantitative biological information, like genomics and proteomics, can impact the clinical management of ovarian cancer.
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23
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He Z, Chen J, Yang F, Pan X, Liu C. Computed tomography-based texture assessment for the differentiation of benign, borderline, and early-stage malignant ovarian neoplasms. J Int Med Res 2023; 51:3000605221150139. [PMID: 36688472 PMCID: PMC9893092 DOI: 10.1177/03000605221150139] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE This study was performed to examine the value of computed tomography-based texture assessment for characterizing different types of ovarian neoplasms. METHODS This retrospective study involved 225 patients with histopathologically confirmed ovarian tumors after surgical resection. Two different data sets of thick (5-mm) slices (during regular and portal venous phases) were analyzed. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis were performed to classify ovarian tumors. The radiologist's misclassification rate was compared with the MaZda (texture analysis software) findings. The results were validated with the neural network classifier. Receiver operating characteristic curves were analyzed to determine the performances of different parameters. RESULTS Nonlinear discriminant analysis had a lower misclassification rate than the other analyses. Thirty texture parameters significantly differed between the two groups. In the training set, WavEnLH_s-3 and WavEnHL_s-3 were the optimal texture features during the regular phase, while WavEnHH_s-4 and Kurtosis seemed to be the most discriminative features during the portal venous phase. In the validation test, benign versus malignant tumors and benign versus borderline lesions were well-distinguished. CONCLUSIONS Computed tomography-based texture features provide a useful imaging signature that may assist in differentiating benign, borderline, and early-stage ovarian cancer.
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Affiliation(s)
- Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Fei Yang
- Department of Clinical Medical, Guangxi Medical University, Nanning, China
| | - Xinwei Pan
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China,Chanzhen Liu, Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Qingxiu District, Nanning 530021, China.
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24
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Jiang ZY, Qi LS, Li JT, Cui N, Li W, Liu W, Wang KZ. Radiomics: Status quo and future challenges. Artif Intell Med Imaging 2022; 3:87-96. [DOI: 10.35711/aimi.v3.i4.87] [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: 09/20/2022] [Revised: 12/08/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Noninvasive imaging (computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography) as an important part of the clinical workflow in the clinic, but it still provides limited information for diagnosis, treatment effect evaluation and prognosis prediction. In addition, judgment and diagnoses made by experts are usually based on multiple years of experience and subjective impression which lead to variable results in the same case. With accumulation of medical imaging data, radiomics emerges as a relatively new approach for analysis. Via artificial intelligence techniques, high-throughput quantitative data which is invisible to the naked eyes extracted from original images can be used in the process of patients’ management. Several studies have evaluated radiomics combined with clinical factors, pathological, or genetic information would assist in the diagnosis, particularly in the prediction of biological characteristics, risk of recurrence, and survival with encouraging results. In various clinical settings, there are limitations and challenges needing to be overcome before transformation. Therefore, we summarize the concepts and method of radiomics including image acquisition, region of interest segmentation, feature extraction and model development. We also set forth the current applications of radiomics in clinical routine. At last, the limitations and related deficiencies of radiomics are pointed out to direct the future opportunities and development.
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Affiliation(s)
- Zhi-Yun Jiang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Li-Shuang Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Jia-Tong Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Nan Cui
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Wei Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
- Department of Interventional Vascular Surgery, The 4th Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
| | - Wei Liu
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Ke-Zheng Wang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
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Prediction Model of Residual Neural Network for Pathological Confirmed Lymph Node Metastasis of Ovarian Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9646846. [PMID: 36267845 PMCID: PMC9578811 DOI: 10.1155/2022/9646846] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 08/31/2022] [Accepted: 09/12/2022] [Indexed: 11/17/2022]
Abstract
Purpose. We want to develop a model for predicting lymph node status based on positron emission computed tomography (PET) images of untreated ovarian cancer patients. We use the feature map formed by wavelet transform and the parameters obtained by image segmentation to build the model. The model is expected to help clinicians and provide additional information about what to do with first-visit patients. Materials and Methods. Our study included 224 patients with ovarian cancer. We have chosen two main methods to extract information from images. On the one hand, we segmented the image to extract the parameters to evaluate the clustering effect. On the other hand, we used wavelet transform to extract the image’s texture information to form the image’s feature map. Based on the above two kinds of information, we used residual neural network and support vector machine for modeling. Results. We established a model to predict lymph node metastasis in patients with primary ovarian cancer using PET images. On the training set, our accuracy was 0.8854, AUC: 0.9472, CI: 0.9098-0.9752, sensitivity was 0.9865, and specificity was 0.7952. On the test set, our accuracy was 0.9104, AUC: 0.9259, CI: 0.8417-0.9889, sensitivity was 0.8125, and specificity was 1.0000. Conclusions. We used wavelet transform to process the preoperative medical images of ovarian cancer patients, and the residual neural network can effectively predict the lymph node metastasis of ovarian cancer patients, which is undoubted of great significance for patients’ staging and treatment options.
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Update on quantitative radiomics of pancreatic tumors. Abdom Radiol (NY) 2022; 47:3118-3160. [PMID: 34292365 DOI: 10.1007/s00261-021-03216-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.
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27
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Huang H, Cheng M, Zhu X. The Effect of Bone Marrow Mesenchymal Stem Cells-Exosomes (BMSC-EXO) on Tumor Angiogenesis and Its Mechanism in Ovarian Cancer Microenvironment. J BIOMATER TISS ENG 2022. [DOI: 10.1166/jbt.2022.3016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
In ovarian cancer microenvironment, BMSC cells can differentiate into a variety of stem cells, thereby reducing the damage to tissues, and this effect lies in the exosomal substances secreted by BMSC cells. Then, in ovarian cancer microenvironment, whether BMSC-exo exhibited an effect
on angiogenesis at the tumor site, and its possible molecular mechanism remains unclear. BALA nude mice and ovarian cancer tumor tissues were collected to isolate vascular endothelial cells which were then assigned into Control group, 40 μg/ml BMSC-exo group, 80 μg/ml BMSC-exo
group, 120 μg/ml BMSC-exo group in the presence of Wnt/β-catenin inhibitor (PNU-74654) followed by analysis of proliferation and migration of ovarian cancer vascular endothelial cells (OCVECs) and the angiogenesis. 40 μg/ml and 80 μg/mlBMSC-exo group
showed significantly higher cell proliferation than control group with higher cell number in 80 μg/ml BMSC-exo group than 40 μg/ml BMSC-exo group (P < 0.05). The number of cell migration after BMSC-exo treatment was increased (P < 0.05) and the tumor
tissue showed obvious angiogenesis with more CD31-positive cells (P < 0.05). PNU-74654 group showed significantly downregulated Wnt and β-catenin proteins (P < 0.05) and lower cell number and higher migration rate of vascular endothelial cells (P <
0.05). In conclusion, exosomes secreted by BMSC can repair damaged tissues possibly through activation of Wnt/β-catenin signaling pathway.
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Affiliation(s)
- Hongli Huang
- Department of Gynecology, Xuancheng People’s Hospital of Anhui Province, Xuancheng City, Anhui, 242000, China
| | - Min Cheng
- Department of Gynecological Oncology, Xuancheng People’s Hospital of Anhui Province, Xuancheng City, Anhui, 242099, China
| | - Xialing Zhu
- Department of Gynecology, Xuancheng People’s Hospital of Anhui Province, Xuancheng City, Anhui, 242000, China
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Feng S, Xia T, Ge Y, Zhang K, Ji X, Luo S, Shen Y. Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer. Front Immunol 2022; 13:868067. [PMID: 35418998 PMCID: PMC8995567 DOI: 10.3389/fimmu.2022.868067] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
Purpose The hypoxic microenvironment is involved in the tumorigenesis of ovarian cancer (OC). Therefore, we aim to develop a non-invasive radiogenomics approach to identify a hypoxia pattern with potential application in patient prognostication. Methods Specific hypoxia-related genes (sHRGs) were identified based on RNA-seq of OC cell lines cultured with different oxygen conditions. Meanwhile, multiple hypoxia-related subtypes were identified by unsupervised consensus analysis and LASSO-Cox regression analysis. Subsequently, diversified bioinformatics algorithms were used to explore the immune microenvironment, prognosis, biological pathway alteration, and drug sensitivity among different subtypes. Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms. Results One hundred forty sHRGs and three types of hypoxia-related subtypes were identified. Among them, hypoxia-cluster-B, gene-cluster-B, and high-risk subtypes had poor survival outcomes. The subtypes were closely related to each other, and hypoxia-cluster-B and gene-cluster-B had higher hypoxia risk scores. Notably, the low-risk subtype had an active immune microenvironment and may benefit from immunotherapy. Finally, a four-feature radiogenomics model was constructed to reveal hypoxia risk status, and the model achieved area under the curve (AUC) values of 0.900 and 0.703 for the training and testing cohorts, respectively. Conclusion As a non-invasive approach, computed tomography-based radiogenomics biomarkers may enable the pretreatment prediction of the hypoxia pattern, prognosis, therapeutic effect, and immune microenvironment in patients with OC.
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Affiliation(s)
- Songwei Feng
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tianyi Xia
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yu Ge
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ke Zhang
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xuan Ji
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shanhui Luo
- Department of Gynaecology, The Second Affiliated Hospital of Soochow University, Soochow University, Suzhou, China
| | - Yang Shen
- Department of Obstetrics and Gynaecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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Nougaret S, Rousset P, Gormly K, Lucidarme O, Brunelle S, Milot L, Salut C, Pilleul F, Arrivé L, Hordonneau C, Baudin G, Soyer P, Brun V, Laurent V, Savoye-Collet C, Petkovska I, Gerard JP, Rullier E, Cotte E, Rouanet P, Beets-Tan RGH, Frulio N, Hoeffel C. Structured and shared MRI staging lexicon and report of rectal cancer: A consensus proposal by the French Radiology Group (GRERCAR) and Surgical Group (GRECCAR) for rectal cancer. Diagn Interv Imaging 2022; 103:127-141. [PMID: 34794932 DOI: 10.1016/j.diii.2021.08.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE To develop French guidelines by experts to standardize data acquisition, image interpretation, and reporting in rectal cancer staging with magnetic resonance imaging (MRI). MATERIALS AND METHODS Evidence-based data and opinions of experts of GRERCAR (Groupe de REcherche en Radiologie sur le CAncer du Rectum [i.e., Rectal Cancer Imaging Research Group]) and GRECCAR (Groupe de REcherche en Chirurgie sur le CAncer du Rectum [i.e., Rectal Cancer Surgery Research Group]) were combined using the RAND-UCLA Appropriateness Method to attain consensus guidelines. Experts scoring of reporting template and protocol for data acquisition were collected; responses were analyzed and classified as "Recommended" versus "Not recommended" (when ≥ 80% consensus among experts) or uncertain (when < 80% consensus among experts). RESULTS Consensus regarding patient preparation, MRI sequences, staging and reporting was attained using the RAND-UCLA Appropriateness Method. A consensus was reached for each reporting template item among the experts. Tailored MRI protocol and standardized report were proposed. CONCLUSION These consensus recommendations should be used as a guide for rectal cancer staging with MRI.
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Affiliation(s)
- Stephanie Nougaret
- Department of Radiology, Institut Régional du Cancer de Montpellier, Montpellier Cancer Research Institute, INSERM U1194, University of Montpellier, 34295, Montpellier, France.
| | - Pascal Rousset
- Department of Radiology, Lyon 1 Claude-Bernard University, 69495 Pierre-Benite, France
| | - Kirsten Gormly
- Dr Jones & Partners Medical Imaging, Kurralta Park, 5037, Australia; University of Adelaide, North Terrace, Adelaide, South Australia 5000, Australia
| | - Oliver Lucidarme
- Department of Radiology, Pitié-Salpêtrière Hospital, Sorbonne Université, 75013 Paris, France; LIB, INSERM, CNRS, UMR7371-U1146, 75013 Paris, France
| | - Serge Brunelle
- Department of Radiology, Institut Paoli-Calmettes, 13009 Marseille, France
| | - Laurent Milot
- Radiology Department, Hospices Civils de Lyon, Lyon Sud University Hospital, 69495 Pierre Bénite, France; Lyon 1 Claude Bernard University, 69100 Villeurbanne, France
| | - Cécile Salut
- Department of Radiology, CHU de Bordeaux, Université de Bordeaux, 33000 Bordeaux, France
| | - Franck Pilleul
- Department of Radiology, Centre Léon Bérard, Lyon, France Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621, Lyon, France
| | - Lionel Arrivé
- Department of Radiology, Hopital St Antoine, Paris, France
| | - Constance Hordonneau
- Department of Radiology, CHU Estaing, Université Clermont-Auvergne, 63000 Clermont-Ferrand, France
| | - Guillaume Baudin
- Department of Radiology, Centre Antoine Lacassagne, 06100 Nice, France
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, AP-HP, 75014 Paris, France; Université de Paris, 75006 Paris, France
| | - Vanessa Brun
- Department of Radiology, CHU Hôpital Pontchaillou, 35000 Rennes Cedex, France
| | - Valérie Laurent
- Department of Radiology, Brabois-Nancy University Hospital, Université de Lorraine, 54500 Vandoeuvre-lès-Nancy, France
| | | | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jean Pierre Gerard
- Department of Radiotherapy, Centre Antoine Lacassagne, 06100 Nice, France
| | - Eric Rullier
- Department of Digestive Surgery, Hôpital Haut-Lévèque, Université de Bordeaux, 33600 Pessac, France
| | - Eddy Cotte
- Department of Digestive Surgery, Hospices Civils de Lyon, Lyon Sud University Hospital, 69310 Pierre Bénite, France; Lyon 1 Claude Bernard University, 69100 Villeurbanne, France
| | - Philippe Rouanet
- Department of surgery, Institut Régional du Cancer de Montpellier, Montpellier Cancer Research Institute, INSERM U1194, University of Montpellier, 34295, Montpellier, France
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, the Netherlands
| | - Nora Frulio
- Department of Radiology, CHU de Bordeaux, Université de Bordeaux, 33000 Bordeaux, France
| | - Christine Hoeffel
- Department of Radiology, Hôpital Robert Debré & CRESTIC, URCA, 51092 Reims, France
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Liu X, Wang T, Zhang G, Hua K, Jiang H, Duan S, Jin J, Zhang H. Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors. J Ovarian Res 2022; 15:22. [PMID: 35115022 PMCID: PMC8815217 DOI: 10.1186/s13048-022-00943-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 12/31/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery. PURPOSE To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods. METHODS A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing. RESULTS The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively. CONCLUSIONS Our results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high ACC.
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Affiliation(s)
- Xuefen Liu
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - Tianping Wang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - Guofu Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - Keqin Hua
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - Hua Jiang
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | | | - Jun Jin
- Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China.
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Tardieu M, Lakhman Y, Khellaf L, Cardoso M, Sgarbura O, Colombo PE, Crispin-Ortuzar M, Sala E, Goze-Bac C, Nougaret S. Assessing Histology Structures by Ex Vivo MR Microscopy and Exploring the Link Between MRM-Derived Radiomic Features and Histopathology in Ovarian Cancer. Front Oncol 2022; 11:771848. [PMID: 35127479 PMCID: PMC8807492 DOI: 10.3389/fonc.2021.771848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/02/2021] [Indexed: 11/14/2022] Open
Abstract
The value of MR radiomic features at a microscopic scale has not been explored in ovarian cancer. The objective of this study was to probe the associations of MR microscopy (MRM) images and MRM-derived radiomic maps with histopathology in high-grade serous ovarian cancer (HGSOC). Nine peritoneal implants from 9 patients with HGSOC were imaged ex vivo with MRM using a 9.4-T MR scanner. All MRM images and computed pixel-wise radiomics maps were correlated with the slice-matched stroma and tumor proportion maps derived from whole histopathologic slide images (WHSI) of corresponding peritoneal implants. Automated MRM-derived segmentation maps of tumor and stroma were constructed using holdout test data and validated against the histopathologic gold standard. Excellent correlation between MRM images and WHSI was observed (Dice index = 0.77). Entropy, correlation, difference entropy, and sum entropy radiomic features were positively associated with high stromal proportion (r = 0.97,0.88, 0.81, and 0.96 respectively, p < 0.05). MR signal intensity, energy, homogeneity, auto correlation, difference variance, and sum average were negatively associated with low stromal proportion (r = -0.91, -0.93, -0.94, -0.9, -0.89, -0.89, respectively, p < 0.05). Using the automated model, MRM predicted stromal proportion with an accuracy ranging from 61.4% to 71.9%. In this hypothesis-generating study, we showed that it is feasible to resolve histologic structures in HGSOC using ex vivo MRM at 9.4 T and radiomics.
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Affiliation(s)
- Marion Tardieu
- Montpellier Cancer Research Institute (IRCM), INSERM U1194, University of Montpellier, Montpellier, France
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Lakhdar Khellaf
- Department of Pathology, Montpellier Cancer Institute (ICM), Montpellier, France
| | - Maida Cardoso
- BNIF Facility, L2C, UMR 5221, CNRS, University of Montpellier, Montpellier, France
| | - Olivia Sgarbura
- Montpellier Cancer Research Institute (IRCM), INSERM U1194, University of Montpellier, Montpellier, France
- Department of Surgery, Montpellier Cancer Institute (ICM), Montpellier, France
| | | | - Mireia Crispin-Ortuzar
- Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Evis Sala
- Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Christophe Goze-Bac
- BNIF Facility, L2C, UMR 5221, CNRS, University of Montpellier, Montpellier, France
| | - Stephanie Nougaret
- Montpellier Cancer Research Institute (IRCM), INSERM U1194, University of Montpellier, Montpellier, France
- Department of Radiology, Montpellier Cancer Institute (ICM), Montpellier, France
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Zhang A, Hu Q, Ma Z, Song J, Chen T. Application of enhanced computed tomography-based radiomics nomogram analysis to differentiate metastatic ovarian tumors from epithelial ovarian tumors. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:1185-1199. [PMID: 36189526 DOI: 10.3233/xst-221244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE To investigate the value of nomogram analysis based on conventional features and radiomics features of computed tomography (CT) venous phase to differentiate metastatic ovarian tumors (MOTs) from epithelial ovarian tumors (EOTs). METHODS A dataset involving 286 patients pathologically confirmed with EOTs (training cohort: 133 cases, validation cohort: 68 cases) and MOTs (training cohort: 54 cases, validation cohort: 31 cases) is assembled in this study. Radiomics features are extracted from the venous phase of CT images. Logistic regression is employed to build models based on conventional features (model 1), radiomics features (model 2), and the combination of model 1 and model 2 (model 3). Diagnostic performance is assessed and compared. Additionally, a nomogram is plotted for model 3, and decision curve analysis is applied for clinical use. RESULTS Age, abdominal metastasis, para-aortic lymph node metastasis, location, and septation are chosen to build Model 1. Ten optimal radiomics features are ultimately selected and radiomics score (rad-score) is calculated to build Model 2. Nomogram score is calculated to build model 3 that shows optimal diagnostic performance in both the training (AUC = 0.952) and validation cohorts (AUC = 0.720), followed by model 1 (AUC = 0.872 for training cohort and AUC = 0.709 for validation cohort) and model 2 (AUC = 0.833 for training cohort and AUC = 0.620 for validation cohort). Additionally, Model 3 achieves accuracy, sensitivity, and specificity of 0.893, 0.880, and 0.926 in the training cohort and 0.737, 0.853, and 0.613 in the validation cohort. CONCLUSION Model 3 demonstrates the best diagnostic performance for preoperative differentiation of MOTs from EOTs. Thus, nomogram analysis based on Model 3 may be used as a biomarker to differentiate MOTs from EOTs.
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Affiliation(s)
- Aining Zhang
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qiming Hu
- Department of Obstetrics & Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhanlong Ma
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiacheng Song
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ting Chen
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Zheng S, Wang H, Han F, Chu J, Zhang F, Zhang X, Shi Y, Zhang L. Detection of Microstructural Medial Prefrontal Cortex Changes Using Magnetic Resonance Imaging Texture Analysis in a Post-Traumatic Stress Disorder Rat Model. Front Psychiatry 2022; 13:805851. [PMID: 35530016 PMCID: PMC9068999 DOI: 10.3389/fpsyt.2022.805851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 03/23/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Radiomics is characterized by high-throughput extraction of texture features from medical images and the mining of information that can potentially be used to define neuroimaging markers in many neurological or psychiatric diseases. However, there have been few studies concerning MRI radiomics in post-traumatic stress disorder (PTSD). The study's aims were to appraise changes in microstructure of the medial prefrontal cortex (mPFC) in a PTSD animal model, specifically single-prolonged stress (SPS) rats, by using MRI texture analysis. The feasibility of using a radiomics approach to classify PTSD rats was examined. METHODS Morris water maze and elevated plus maze were used to assess behavioral changes in the rats. Two hundred and sixty two texture features were extracted from each region of interest in T2-weighted images. Stepwise discriminant analysis (SDA) and LASSO regression were used to perform feature selection and radiomics signature building to identify mPFC radiomics signatures consisting of optimal features, respectively. Receiver operating characteristic curve plots were used to evaluate the classification performance. Immunofluorescence techniques were used to examine the expression of glial fibrillary acidic protein (GFAP) and neuronal nuclei (NeuN) in the mPFC. Nuclear pycnosis was detected using 4',6-diamidino-2-phenylindole (DAPI) staining. RESULTS Behavioral results indicated decreased learning and spatial memory performance and increased anxiety-like behavior after SPS stimulation. SDA analysis showed that the general non-cross-validated and cross-validated discrimination accuracies were 86.5% and 80.4%. After LASSO dimensionality reduction, 10 classification models were established. For classifying PTSD rats between the control and each SPS group, these models achieved AUCs of 0.944, 0.950, 0.959, and 0.936. Among four SPS groups, the AUCs were 0.927, 0.943, 0.967, 0.916, 0.932, and 0.893, respectively. The number of GFAP-positive cells and intensity of GFAP-IR within the mPFC increased 1 day after SPS treatment, and then decreased. The intensity of NeuN-IR and number of NeuN-positive cells significantly decreased from 1 to 14 days after SPS stimulation. The brightness levels of DAPI-stained nuclei increased in SPS groups. CONCLUSION Non-invasive MRI radiomics features present an efficient and sensitive way to detect microstructural changes in the mPFC after SPS stimulation, and they could potentially serve as a novel neuroimaging marker in PTSD diagnosis.
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Affiliation(s)
- Shilei Zheng
- Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Han Wang
- Medical Imaging Center, Taian Central Hospital, Taian, China
| | - Fang Han
- Post-Traumatic Stress Disorder Laboratory, Department of Histology and Embryology, Basic Medical Sciences College, China Medical University, Shenyang, China
| | - Jianyi Chu
- Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Fan Zhang
- Department of Neurology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Xianglin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Yuxiu Shi
- Post-Traumatic Stress Disorder Laboratory, Department of Histology and Embryology, Basic Medical Sciences College, China Medical University, Shenyang, China
| | - Lili Zhang
- Department of Stomatology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
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Can we use radiomics in ultrasound imaging? Impact of preprocessing on feature repeatability. Diagn Interv Imaging 2021; 102:659-667. [PMID: 34690106 DOI: 10.1016/j.diii.2021.10.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/07/2021] [Accepted: 10/07/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE The purpose of this study was to assess the inter-slice radiomic feature repeatability in ultrasound imaging and the impact of preprocessing using intensity standardization and grey-level discretization to help improve radiomics reproducibility. MATERIALS AND METHODS This single-center study enrolled consecutive patients with an orbital lesion who underwent ultrasound examination of the orbit from December 2015 to July 2019. Two images per lesion were randomly assigned to two subsets. Radiomic features were extracted and inter-slice repeatability was assessed using the intraclass correlation coefficient (ICC) between the subsets. The impact of preprocessing on feature repeatability was assessed using image intensity standardization with or without outliers removal on whole images, bounding boxes or regions of interest (ROI), and fixed bin size or fixed bin number grey-level discretization. Number of inter-slice repeatable features (ICC ≥0.7) between methods was compared. RESULTS Eighty-eight patients (37 men, 51 women) with a mean age of 51.5 ± 17 (SD) years (range: 20-88 years) were enrolled. Without preprocessing, 29/101 features (28.7%) were repeatable between slices. The greatest number of repeatable features (41/101) was obtained using intensity standardization with outliers removal on the ROI and fixed bin size discretization. Standardization performed better with outliers removal than without (P < 0.001), and on ROIs than on native images (P < 0.001). Fixed bin size discretization performed better than fixed bin number (P = 0.008). CONCLUSION Radiomic features extracted from ultrasound images are impacted by the slice and preprocessing. The use of intensity standardization with outliers removal applied to the ROI and a fixed bin size grey-level discretization may improve feature repeatability.
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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Bonnin A, Durot C, Barat M, Djelouah M, Grange F, Mulé S, Soyer P, Hoeffel C. CT texture analysis as a predictor of favorable response to anti-PD1 monoclonal antibodies in metastatic skin melanoma. Diagn Interv Imaging 2021; 103:97-102. [PMID: 34666945 DOI: 10.1016/j.diii.2021.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE The purpose of this study was to determine whether texture analysis features on pretreatment contrast-enhanced computed tomography (CT) images and their evolution can predict treatment response of metastatic skin melanoma (SM) treated with anti-PD1 monoclonal antibodies. MATERIALS AND METHODS Sixty patients (29 men, 31 women; median age, 56 years; age range: 27-91 years) with metastatic SM treated with pembrolizumab (43/60; 72%) or nivolumab (17/60; 28%) were included. Texture analysis of SM metastases was performed on baseline and first post-treatment evaluation CT examinations. Mean gray-level, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales, ranging from fine to coarse. Lasso penalized Cox regression analyses were performed to identify independent variables associated with favorable response to treatment. RESULTS A total of 127 metastases were analyzed, with a median of two metastases per patient. Skewness at fine texture scale (spatial scale filtration [SSF] = 2; Hazard ratio [HR]: 3.51; 95% CI: 2.08-8.57; P = 0.010), skewness at medium texture scale (SSF = 3; HR: 0.56; 95% CI: 0.11-1.59; P = 0.014), variation of entropy at fine texture scale (SSF = 2; HR: 37.76; 95% CI: 3.48-496.22; P = 0.008) and LDH above the threshold of 248 UI/L (HR: 3.56; 95% CI: 1.78-21.35; P = 0.032] were independent predictors of response to treatment. CONCLUSION Pretreatment CT texture analysis-derived tumor skewness and variation of entropy between baseline and first control CT examination may be used as predictors of favorable response to anti-PD1 monoclonal antibodies in patients with metastatic SM.
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Affiliation(s)
- Angèle Bonnin
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Carole Durot
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Maxime Barat
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Manel Djelouah
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Florent Grange
- Department of Dermatology, Valence Hospital, 26000 Valence, France
| | - Sébastien Mulé
- Department of Radiology, Henri Mondor University Hospital, APH-HP, 94000 Créteil, France
| | - Philippe Soyer
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Christine Hoeffel
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; CRESTIC, Reims Champagne-Ardenne University, 51000 Reims, France.
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Chen HZ, Wang XR, Zhao FM, Chen XJ, Li XS, Ning G, Guo YK. The Development and Validation of a CT-Based Radiomics Nomogram to Preoperatively Predict Lymph Node Metastasis in High-Grade Serous Ovarian Cancer. Front Oncol 2021; 11:711648. [PMID: 34532289 PMCID: PMC8438232 DOI: 10.3389/fonc.2021.711648] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 08/09/2021] [Indexed: 01/04/2023] Open
Abstract
Purpose To develop and validate a radiomics model for predicting preoperative lymph node (LN) metastasis in high-grade serous ovarian cancer (HGSOC). Materials and Methods From May 2008 to January 2018, a total of 256 eligible HGSOC patients who underwent tumor resection and LN dissection were divided into a training cohort (n=179) and a test cohort (n=77) in a 7:3 ratio. A Radiomics Model was developed based on a training cohort of 179 patients. A radiomics signature (defined as the Radscore) was selected by using the random forest method. Logistics regression was used as the classifier for modeling. An Integrated Model that incorporated the Radscore and CT_reported LN status (CT_LN_report) was developed and presented as a radiomics nomogram. Its performance was determined by the area under the curve (AUC), calibration, and decision curve. The radiomics nomogram was internally tested in an independent test cohort (n=77) and a CT-LN-report negative subgroup (n=179) using the formula derived from the training cohort. Results The AUC value of the CT_LN_report was 0.688 (95% CI: 0.626, 0.759) in the training cohort and 0.717 (95% CI: 0.630, 0.804) in the test cohort. The Radiomics Model yielded an AUC of 0.767 (95% CI: 0.696, 0.837) in the training cohort and 0.753 (95% CI: 0.640, 0.866) in the test. The radiomics nomogram demonstrated favorable calibration and discrimination in the training cohort (AUC=0.821), test cohort (AUC=0.843), and CT-LN-report negative subgroup (AUC=0.82), outperforming the Radiomics Model and CT_LN_report alone. Conclusions The radiomics nomogram derived from portal phase CT images performed well in predicting LN metastasis in HGSOC and could be recommended as a new, convenient, and non-invasive method to aid in clinical decision-making.
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Affiliation(s)
- Hui-Zhu Chen
- Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
| | | | - Fu-Min Zhao
- Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Xi-Jian Chen
- Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Xue-Sheng Li
- Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Gang Ning
- Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Ying-Kun Guo
- Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
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Arezzo F, Loizzi V, La Forgia D, Moschetta M, Tagliafico AS, Cataldo V, Kawosha AA, Venerito V, Cazzato G, Ingravallo G, Resta L, Cicinelli E, Cormio G. Radiomics Analysis in Ovarian Cancer: A Narrative Review. APPLIED SCIENCES 2021; 11:7833. [DOI: 10.3390/app11177833] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2024]
Abstract
Ovarian cancer (OC) is the second most common gynecological malignancy, accounting for about 14,000 deaths in 2020 in the US. The recognition of tools for proper screening, early diagnosis, and prognosis of OC is still lagging. The application of methods such as radiomics to medical images such as ultrasound scan (US), computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) in OC may help to realize so-called “precision medicine” by developing new quantification metrics linking qualitative and/or quantitative data imaging to achieve clinical diagnostic endpoints. This narrative review aims to summarize the applications of radiomics as a support in the management of a complex pathology such as ovarian cancer. We give an insight into the current evidence on radiomics applied to different imaging methods.
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Affiliation(s)
- Francesca Arezzo
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Vera Loizzi
- Obstetrics and Gynecology Unit, Interdisciplinar Department of Medicine, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Daniele La Forgia
- SSD Radiodiagnostica Senologica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124 Bari, Italy
| | - Marco Moschetta
- Breast Care Unit, Department of Emergency and Organ Transplantation, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Alberto Stefano Tagliafico
- Radiology Section, Department of Health Sciences (DISSAL), University of Genova, Via L.B. Alberti 2, 16132 Genoa, Italy
| | - Viviana Cataldo
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Adam Abdulwakil Kawosha
- Department of General Medicine, Universitatea Medicina si Farmacie Grigore T Popa, Strada Universitatii 16, 700115 Iasi, Romania
| | - Vincenzo Venerito
- Rheumatology Unit, Department of Emergency and Organ Transplantations, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Gerardo Cazzato
- Pathology Section, Department of Emergency and Organ Transplantation, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Giuseppe Ingravallo
- Pathology Section, Department of Emergency and Organ Transplantation, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Leonardo Resta
- Pathology Section, Department of Emergency and Organ Transplantation, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Ettore Cicinelli
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
| | - Gennaro Cormio
- Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
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Chiappa V, Interlenghi M, Bogani G, Salvatore C, Bertolina F, Sarpietro G, Signorelli M, Ronzulli D, Castiglioni I, Raspagliesi F. A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125. Eur Radiol Exp 2021; 5:28. [PMID: 34308487 PMCID: PMC8310829 DOI: 10.1186/s41747-021-00226-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 05/21/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND To evaluate the performance of a decision support system (DSS) based on radiomics and machine learning in predicting the risk of malignancy of ovarian masses (OMs) from transvaginal ultrasonography (TUS) and serum CA-125. METHODS A total of 274 consecutive patients who underwent TUS (by different examiners and with different ultrasound machines) and surgery, with suspicious OMs and known CA-125 serum level were used to train and test a DSS. The DSS was used to predict the risk of malignancy of these masses (very low versus medium-high risk), based on the US appearance (solid, liquid, or mixed) and radiomic features (morphometry and regional texture features) within the masses, on the shadow presence (yes/no), and on the level of serum CA-125. Reproducibility of results among the examiners, and performance accuracy, sensitivity, specificity, and area under the curve were tested in a real-world clinical setting. RESULTS The DSS showed a mean 88% accuracy, 99% sensitivity, and 77% specificity for the 239 patients used for training, cross-validation, and testing, and a mean 91% accuracy, 100% sensitivity, and 80% specificity for the 35 patients used for independent testing. CONCLUSIONS This DSS is a promising tool in women diagnosed with OMs at TUS, allowing to predict the individual risk of malignancy, supporting clinical decision making.
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Affiliation(s)
- Valentina Chiappa
- Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Venezian 1, 20133, Milan, Italy
| | | | - Giorgio Bogani
- Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Venezian 1, 20133, Milan, Italy
| | | | - Francesca Bertolina
- Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Venezian 1, 20133, Milan, Italy
| | - Giuseppe Sarpietro
- Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Venezian 1, 20133, Milan, Italy
| | - Mauro Signorelli
- Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Venezian 1, 20133, Milan, Italy
| | - Dominique Ronzulli
- Clinical Trial Center, Fondazione IRCCS Istituto Nazionale Tumori di Milano, Milan, Italy
| | | | - Francesco Raspagliesi
- Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Venezian 1, 20133, Milan, Italy
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Manganaro L, Nicolino GM, Dolciami M, Martorana F, Stathis A, Colombo I, Rizzo S. Radiomics in cervical and endometrial cancer. Br J Radiol 2021; 94:20201314. [PMID: 34233456 PMCID: PMC9327743 DOI: 10.1259/bjr.20201314] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer. Nonetheless, cervical cancer is the most frequent gynecological malignancy in developing countries and endometrial cancer is the most common in western countries. The purpose of this narrative review is to give an overview of the latest published papers evaluating the role of radiomics in cervical and endometrial cancer, mostly evaluating association with tumor prognostic factors, with response to therapy and with prediction of recurrence and distant metastasis.
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Affiliation(s)
- Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Gabriele Maria Nicolino
- Post-graduate School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, Milan, Italy
| | - Miriam Dolciami
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Federica Martorana
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Anastasios Stathis
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland.,Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland
| | - Ilaria Colombo
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Stefania Rizzo
- Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland.,Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale, Via Tesserete 46, Lugano (CH), Switzerland
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Value of Quantitative CTTA in Differentiating Malignant From Benign Bosniak III Renal Lesions on CT Images. J Comput Assist Tomogr 2021; 45:528-536. [PMID: 34176873 DOI: 10.1097/rct.0000000000001181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE The aim of this study was to investigate whether computed tomography texture analysis can differentiate malignant from benign Bosniak III renal lesions on computed tomography (CT) images. METHODS This retrospective case-control study included 45 patients/lesions (22 benign and 23 malignant lesions) with Bosniak III renal lesions who underwent CT examination. Axial image slices in the unenhanced phase, corticomedullary phase, and nephrographic phase were selected and delineated manually. Computed tomography texture analysis was performed on each lesion during these 3 phases. Histogram-based, gray-level co-occurrence matrix, and gray-level run-length matrix features were extracted using open-source software and analyzed. In addition, receiver operating characteristic curve was constructed, and the area under the receiver operating characteristic curve (AUC) of each feature was constructed. RESULTS Of the 33 extracted features, 16 features showed significant differences (P < 0.05). Eight features were significantly different between the 2 groups after Holm-Bonferroni correction, including 3 histogram-based, 4 gray-level co-occurrence matrix, and 1 gray-level run-length matrix features (P < 0.01). The texture features resulted in the highest AUC of 0.769 ± 0.074. Renal cell carcinomas were labeled with a higher degree of lesion gray-level disorder and lower lesion homogeneity, and a model incorporating the 3 most discriminative features resulted in an AUC of 0.846 ± 0.058. CONCLUSIONS The results of this study showed that CT texture features were related to malignancy in Bosniak III renal lesions. Computed tomography texture analysis might help in differentiating malignant from benign Bosniak III renal lesions on CT images.
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Nougaret S, Cunha TM. From ovary to brain: Specific imaging features of ovarian teratomas associated with anti-N-methyl-d-aspartate receptor encephalitis. Diagn Interv Imaging 2021; 102:403-404. [PMID: 34147389 DOI: 10.1016/j.diii.2021.04.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 04/25/2021] [Indexed: 12/28/2022]
Affiliation(s)
- Stephanie Nougaret
- Department of Radiology, Institut Régional du Cancer de Montpellier, Montpellier Cancer Research institute, INSERM, U1194, University of Montpellier, 34295 Montpellier, France.
| | - Teresa Margarida Cunha
- Department of Radiology, Instituto Português de Oncologia de Lisboa Francisco Gentil, 1099-023 Lisboa, Portugal
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Crombé A, Gauquelin L, Nougaret S, Chicart M, Pulido M, Floquet A, Guyon F, Croce S, Kind M, Cazeau AL. Diffusion-weighted MRI and PET/CT reproducibility in epithelial ovarian cancers during neoadjuvant chemotherapy. Diagn Interv Imaging 2021; 102:629-639. [PMID: 34112625 DOI: 10.1016/j.diii.2021.05.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 01/12/2023]
Abstract
PURPOSE To investigate the reproducibility of diffusion-weighted (DW) MRI and 18F-Fluorodeoxyglucose (18F-FDG)-Positron emission tomography/CT (PET/CT) in monitoring response to neoadjuvant chemotherapy in epithelial ovarian cancer. MATERIALS AND METHODS Ten women (median age, 67 years; range: 41.8-77.3 years) with stage IIIC-IV epithelial ovarian cancers were included in this prospective trial (NCT02792959) between 2014 and 2016. All underwent initial laparoscopic staging, four cycles of carboplatine-paclitaxel-based chemotherapy and interval debulking surgery. PET/CT and DW-MRI were performed at baseline (C0), after one cycle (C1) and before surgery (C4). Two nuclear physicians and two radiologists assessed five anatomic sites for the presence of ≥1 lesion. Target lesions in each site were defined and their apparent diffusion coefficient (ADC), maximal standardized uptake value (SUV-max), SUV-mean, SUL-peak, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were monitored (i.e., 10 patients ×5 sites ×3 time-points). Their relative early and late changes were calculated. Intra/inter-observer reproducibilities of qualitative and quantitative analysis were estimated with Kappa and intra-class correlation coefficients (ICCs). RESULTS For both modalities, inter- and intra-observer agreement percentages were excellent for initial staging but declined later for DW-MRI, leading to lower Kappa values for inter- and intra-observer variability (0.949 and 1 at C0, vs. 0.633 and 0.643 at C4, respectively) while Kappa values remained>0.8 for PET/CT. Inter- and intra-observer ICCs were>0.75 for SUV-max, SUL-peak, SUV-mean and their change regardless the time-point. ADC showed lower ICCs (range: 0.013-0.811). ANOVA found significant influences of the evaluation time, the measurement used (ADC, SUV-max, SUV-mean, SUV-max, SUL-peak, MTV or TLG) and their interaction on ICC values (P=0.0023, P<0.0001 and P =0.0028, respectively). CONCLUSION While both modalities demonstrated high reproducibility at baseline, only SUV-max, SUL-peak, SUV-mean and their changes maintained high reproducibility during chemotherapy.
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Affiliation(s)
- Amandine Crombé
- Department of Oncologic Imaging, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France; Bordeaux University, 33000 Bordeaux, France; Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, 33405, Talence, France.
| | - Lisa Gauquelin
- Department of Biostatistics, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Stéphanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, University of Montpellier, 34090 Montpellier, France
| | - Marine Chicart
- Department of nuclear medicine, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Marina Pulido
- Department of Biostatistics, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Anne Floquet
- Department of Medical Oncology, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Frédéric Guyon
- Department of Oncological Surgery, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Sabrina Croce
- Department of Pathology, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Michèle Kind
- Department of Oncologic Imaging, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Anne-Laure Cazeau
- Department of nuclear medicine, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
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A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer. Abdom Radiol (NY) 2021; 46:2384-2392. [PMID: 34086094 PMCID: PMC8205899 DOI: 10.1007/s00261-021-03120-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/27/2021] [Accepted: 05/06/2021] [Indexed: 12/19/2022]
Abstract
Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated. Results Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram. Conclusion The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.
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45
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Nougaret S, McCague C, Tibermacine H, Vargas HA, Rizzo S, Sala E. Radiomics and radiogenomics in ovarian cancer: a literature review. Abdom Radiol (NY) 2021; 46:2308-2322. [PMID: 33174120 DOI: 10.1007/s00261-020-02820-z] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/01/2020] [Accepted: 10/10/2020] [Indexed: 01/25/2023]
Abstract
Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. Standard imaging evaluation using CT or MRI does not take into account this tumoral heterogeneity especially in advanced stages with peritoneal carcinomatosis. As such, newly emergent fields in the assessment of tumor heterogeneity have been proposed using radiomics to evaluate the whole tumor burden heterogeneity as opposed to single biopsy sampling. This review provides an overview of radiomics, radiogenomics, and proteomics and examines the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer.
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Affiliation(s)
- S Nougaret
- IRCM, Montpellier Cancer Research Institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France. .,Department of Radiology, Montpellier Cancer institute, 208 Ave des Apothicaires, 34295, Montpellier, France.
| | - Cathal McCague
- Department of Radiology, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Hichem Tibermacine
- IRCM, Montpellier Cancer Research Institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France.,Department of Radiology, Montpellier Cancer institute, 208 Ave des Apothicaires, 34295, Montpellier, France
| | - Hebert Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Stefania Rizzo
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, CH, Switzerland.,Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Lugano, CH, Switzerland
| | - E Sala
- Department of Radiology, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
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46
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Park H, Qin L, Guerra P, Bay CP, Shinagare AB. Decoding incidental ovarian lesions: use of texture analysis and machine learning for characterization and detection of malignancy. Abdom Radiol (NY) 2021; 46:2376-2383. [PMID: 32728871 DOI: 10.1007/s00261-020-02668-3] [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: 03/28/2020] [Revised: 07/11/2020] [Accepted: 07/17/2020] [Indexed: 12/27/2022]
Abstract
PURPOSE To compare CT texture features of benign and malignant ovarian lesions and to build a machine learning model to detect malignancy in incidental ovarian lesions. METHODS In this IRB-approved, HIPAA-compliant, retrospective study, 427 consecutive patients with incidental ovarian lesions detected on contrast-enhanced CT (348, 81.5% benign and 79, 18.5% malignant) were included. The following CT texture features were analyzed using commercially available software (TexRAD, Feedback Plc, Cambridge, UK): total pixel, mean, standard deviation (SD), entropy, mean value of positive pixels (MPP), skewness, kurtosis and entropy. Three machine learning models were created by combining texture features and patients' age, and performance of these models was assessed using tenfold cross-validation. Receiver operating characteristics (ROC) were constructed to assess sensitivity and specificity. The cutoff value was picked using a cost-weighted method. RESULTS Total pixels, mean, SD, entropy, MPP, and skewness were significantly different between benign and malignant groups (p < 0.05). With a selected 10 as a cost factor to optimize cutoff value selection, sensitivity 92%, specificity 60% in the random forest (RF) model, sensitivity 91%, specificity 69% in SVM model, and sensitivity 92%, specificity 61% in the logistic regression, respectively. CONCLUSION CT texture analysis could provide objective imaging analysis of incidental ovarian lesions and ML models using CT texture features and age demonstrated high sensitivity and moderate specificity for detection of malignant lesions.
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47
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Virarkar M, Ganeshan D, Gulati AT, Palmquist S, Iyer R, Bhosale P. Diagnostic performance of PET/CT and PET/MR in the management of ovarian carcinoma-a literature review. Abdom Radiol (NY) 2021; 46:2323-2349. [PMID: 33175199 DOI: 10.1007/s00261-020-02847-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 10/25/2020] [Accepted: 10/29/2020] [Indexed: 12/17/2022]
Abstract
Ovarian cancer is a challenging disease. It often presents at an advanced stage with frequent recurrence despite optimal management. Accurate staging and restaging are critical for improving treatment outcomes and determining the prognosis. Imaging is an indispensable component of ovarian cancer management. Hybrid imaging modalities, including positron emission tomography/computed tomography (PET/CT) and PET/magnetic resonance imaging (MRI), are emerging as potential non-invasive imaging tools for improved management of ovarian cancer. This review article discusses the role of PET/CT and PET/MRI in ovarian cancer.
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Affiliation(s)
- Mayur Virarkar
- Department of Diagnostic Radiology, Unit 1476, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Dhakshinamoorthy Ganeshan
- Department of Diagnostic Radiology, Unit 1476, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Anjalie Tara Gulati
- BS, Anthropology and Global Health, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Sarah Palmquist
- Department of Diagnostic Radiology, Unit 1476, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Revathy Iyer
- Department of Diagnostic Radiology, Unit 1476, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Priya Bhosale
- Department of Diagnostic Radiology, Unit 1476, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
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Li Q, Dong F, Jiang B, Zhang M. Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics. Front Oncol 2021; 11:646267. [PMID: 34109112 PMCID: PMC8182051 DOI: 10.3389/fonc.2021.646267] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 04/26/2021] [Indexed: 01/01/2023] Open
Abstract
Objectives To explore the magnetic resonance imaging (MRI) characteristics of brain diffuse midline gliomas with the H3 K27M mutation (DMG-M) using radiomics. Materials and Methods Thirty patients with diffuse midline gliomas, including 16 with the H3 K27M mutant and 14 with wild type tumors, were retrospectively included in this study. A total of 272 radiomic features were initially extracted from MR images of each tumor. Principal component analysis, univariate analysis, and three other feature selection methods, including variance thresholding, recursive feature elimination, and the elastic net, were used to analyze the radiomic features. Based on the results, related visually accessible features of the tumors were further evaluated. Results Patients with DMG-M were younger than those with diffuse midline gliomas with H3 K27M wild (DMG-W) (median, 25.5 and 48 years old, respectively; p=0.005). Principal component analysis showed that there were obvious overlaps in the first two principal components for both DMG-M and DMG-W tumors. The feature selection results showed that few features from T2-weighted images (T2WI) were useful for differentiating DMG-M and DMG-W tumors. Thereafter, four visually accessible features related to T2WI were further extracted and analyzed. Among these features, only cystic formation showed a significant difference between the two types of tumors (OR=7.800, 95% CI 1.476-41.214, p=0.024). Conclusions DMGs with and without the H3 K27M mutation shared similar MRI characteristics. T2W sequences may be valuable, and cystic formation a useful MRI biomarker, for diagnosing brain DMG-M.
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Affiliation(s)
- Qian Li
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fei Dong
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Biao Jiang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Kido A, Nakamoto Y. Implications of the new FIGO staging and the role of imaging in cervical cancer. Br J Radiol 2021; 94:20201342. [PMID: 33989030 DOI: 10.1259/bjr.20201342] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
International Federation of Gynecology and Obstetrics (FIGO) staging, which is the fundamentally important cancer staging system for cervical cancer, has changed in 2018. New FIGO staging includes considerable progress in the incorporation of imaging findings for tumour size measurement and evaluating lymph node (LN) metastasis in addition to tumour extent evaluation. MRI with high spatial resolution is expected for tumour size measurements and the high accuracy of positron emmision tomography/CT for LN evaluation. The purpose of this review is firstly review the diagnostic ability of each imaging modality with the clinical background of those two factors newly added and the current state for LN evaluation. Secondly, we overview the fundamental imaging findings with characteristics of modalities and sequences in MRI for accurate diagnosis depending on the focus to be evaluated and for early detection of recurrent tumour. In addition, the role of images in treatment response and prognosis prediction is given with the development of recent technique of image analysis including radiomics and deep learning.
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Affiliation(s)
- Aki Kido
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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50
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Li S, Liu J, Xiong Y, Pang P, Lei P, Zou H, Zhang M, Fan B, Luo P. A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography. Sci Rep 2021; 11:8730. [PMID: 33888749 PMCID: PMC8062553 DOI: 10.1038/s41598-021-87775-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/05/2021] [Indexed: 12/13/2022] Open
Abstract
This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.
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Affiliation(s)
- Shiyun Li
- Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Jiaqi Liu
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Yuanhuan Xiong
- Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | | | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, China
| | - Huachun Zou
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Mei Zhang
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China.
| | - Puying Luo
- Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China.
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