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Lyu GW, Tong T, Yang GD, Zhao J, Xu ZF, Zheng N, Zhang ZF. Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology. Front Med (Lausanne) 2024; 11:1501652. [PMID: 39610679 PMCID: PMC11602298 DOI: 10.3389/fmed.2024.1501652] [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: 09/25/2024] [Accepted: 10/28/2024] [Indexed: 11/30/2024] Open
Abstract
Background Radiomics, which involves the conversion of digital images into high-dimensional data, has been used in oncological studies since 2012. We analyzed the publications that had been conducted on this subject using bibliometric and visual methods to expound the hotpots and future trends regarding radiomics in evaluating lymph node status in oncology. Methods Documents published between 2012 and 2023, updated to August 1, 2024, were searched using the Scopus database. VOSviewer, R Package, and Microsoft Excel were used for visualization. Results A total of 898 original articles and reviews written in English and be related to radiomics for evaluating lymph node status in oncology, published between 2015 and 2023, were retrieved. A significant increase in the number of publications was observed, with an annual growth rate of 100.77%. The publications predominantly originated from three countries, with China leading in the number of publications and citations. Fudan University was the most contributing affiliation, followed by Sun Yat-sen University and Southern Medical University, all of which were from China. Tian J. from the Chinese Academy of Sciences contributed the most within 5885 authors. In addition, Frontiers in Oncology had the most publications and transcended other journals in recent 4 years. Moreover, the keywords co-occurrence suggested that the interplay of "radiomics" and "lymph node metastasis," as well as "major clinical study" were the predominant topics, furthermore, the focused topics shifted from revealing the diagnosis of cancers to exploring the deep learning-based prediction of lymph node metastasis, suggesting the combination of artificial intelligence research would develop in the future. Conclusion The present bibliometric and visual analysis described an approximately continuous trend of increasing publications related to radiomics in evaluating lymph node status in oncology and revealed that it could serve as an efficient tool for personalized diagnosis and treatment guidance in clinical patients, and combined artificial intelligence should be further considered in the future.
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Affiliation(s)
- Gui-Wen Lyu
- Department of Radiology, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Tong Tong
- Department of Ultrasound, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Gen-Dong Yang
- Department of Radiology, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Jing Zhao
- Department of Radiology, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Zi-Fan Xu
- Department of Pathology, Shenzhen University Medical School, Shenzhen, China
| | - Na Zheng
- Department of Pathology, Shenzhen University Medical School, Shenzhen, China
| | - Zhi-Fang Zhang
- Department of Radiology, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
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Aouadi S, Torfeh T, Bouhali O, Yoganathan SA, Paloor S, Chandramouli S, Hammoud R, Al-Hammadi N. Prediction of cervix cancer stage and grade from diffusion weighted imaging using EfficientNet. Biomed Phys Eng Express 2024; 10:045042. [PMID: 38815562 DOI: 10.1088/2057-1976/ad5207] [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: 01/15/2024] [Accepted: 05/30/2024] [Indexed: 06/01/2024]
Abstract
Purpose. This study aims to introduce an innovative noninvasive method that leverages a single image for both grading and staging prediction. The grade and the stage of cervix cancer (CC) are determined from diffusion-weighted imaging (DWI) in particular apparent diffusion coefficient (ADC) maps using deep convolutional neural networks (DCNN).Methods. datasets composed of 85 patients having annotated tumor stage (I, II, III, and IV), out of this, 66 were with grade (II and III) and the remaining patients with no reported grade were retrospectively collected. The study was IRB approved. For each patient, sagittal and axial slices containing the gross tumor volume (GTV) were extracted from ADC maps. These were computed using the mono exponential model from diffusion weighted images (b-values = 0, 100, 1000) that were acquired prior to radiotherapy treatment. Balanced training sets were created using the Synthetic Minority Oversampling Technique (SMOTE) and fed to the DCNN. EfficientNetB0 and EfficientNetB3 were transferred from the ImageNet application to binary and four-class classification tasks. Five-fold stratified cross validation was performed for the assessment of the networks. Multiple evaluation metrics were computed including the area under the receiver operating characteristic curve (AUC). Comparisons with Resnet50, Xception, and radiomic analysis were performed.Results. for grade prediction, EfficientNetB3 gave the best performance with AUC = 0.924. For stage prediction, EfficientNetB0 was the best with AUC = 0.931. The difference between both models was, however, small and not statistically significant EfficientNetB0-B3 outperformed ResNet50 (AUC = 0.71) and Xception (AUC = 0.89) in stage prediction, and demonstrated comparable results in grade classification, where AUCs of 0.89 and 0.90 were achieved by ResNet50 and Xception, respectively. DCNN outperformed radiomic analysis that gave AUC = 0.67 (grade) and AUC = 0.66 (stage).Conclusion.the prediction of CC grade and stage from ADC maps is feasible by adapting EfficientNet approaches to the medical context.
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Affiliation(s)
- Souha Aouadi
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Tarraf Torfeh
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Othmane Bouhali
- Department of Science, Texas A&M University at Qatar, PO Box 23874, Education City, Doha, Qatar
| | - S A Yoganathan
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Satheesh Paloor
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Suparna Chandramouli
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Rabih Hammoud
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Noora Al-Hammadi
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
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Tang Q, Zhou Q, Chen W, Sang L, Xing Y, Liu C, Wang K, Liu WV, Xu L. A feasibility study of reduced full-of-view synthetic high-b-value diffusion-weighted imaging in uterine tumors. Insights Imaging 2023; 14:12. [PMID: 36645541 PMCID: PMC9842823 DOI: 10.1186/s13244-022-01350-0] [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: 06/12/2022] [Accepted: 12/05/2022] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVES This study aimed to evaluate the feasibility of reduced full-of-view synthetic high-b value diffusion-weighted images (rFOV-syDWIs) in the clinical application of cervical cancer based on image quality and diagnostic efficacy. METHODS We retrospectively evaluated the data of 35 patients with cervical cancer and 35 healthy volunteers from May to November 2021. All patients and volunteers underwent rFOV-DWI scans, including a 13b-protocol: b = 0, 25, 50, 75, 100, 150, 200, 400, 600, 800, 1000, 1200, and 1500 s/mm2 and a 5b-protocol: b = 0, 100, 400, 800,1500 s/mm2. rFOV-syDWIs with b values of 1200 (rFOV-syDWIb=1200) and 1500 (rFOV-syDWIb=1500) were generated from two different multiple-b-value image datasets using a mono-exponential fitting algorithm. According to homoscedasticity and normality assessed by the Levene's test and Shapiro-Wilk test, the inter-modality differences of quantitative measurements were, respectively, examined by Wilcoxon signed-rank test or paired t test and the inter-group differences of ADC values were examined by independent t test or Mann-Whitney U test. RESULTS A higher inter-reader agreement between SNRs and CNRs was found in 13b-protocol and 5b-protocol rFOV-syDWIb=1200/1500 compared to 13b-protocol rFOV-sDWIb=1200/1500 (p < 0.05). AUC of 5b-protocol syADCmean,b=1200/1500 and syADCminimum,b=1200/1500 was equal or higher than that of 13b-protocol sADCmean,b=1200/1500 and sADCminimum,b=1200/1500. CONCLUSIONS rFOV-syDWIs provide better lesion clarity and higher image quality than rFOV-sDWIs. 5b-protocol rFOV-syDWIs shorten scan time, and synthetic ADCs offer reliable diagnosis value as scanned 13b-protocol DWIs.
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Affiliation(s)
- Qian Tang
- grid.443573.20000 0004 1799 2448Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei China ,grid.443573.20000 0004 1799 2448Biomedical Engineering College, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei China
| | - Qiqi Zhou
- grid.443573.20000 0004 1799 2448Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei China
| | - Wen Chen
- grid.443573.20000 0004 1799 2448Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei China
| | - Ling Sang
- grid.443573.20000 0004 1799 2448Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei China
| | - Yu Xing
- grid.443573.20000 0004 1799 2448Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei China
| | - Chao Liu
- grid.443573.20000 0004 1799 2448Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei China
| | - Kejun Wang
- grid.443573.20000 0004 1799 2448Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei China
| | | | - Lin Xu
- grid.443573.20000 0004 1799 2448Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei China
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Ciulla S, Celli V, Aiello AA, Gigli S, Ninkova R, Miceli V, Ercolani G, Dolciami M, Ricci P, Palaia I, Catalano C, Manganaro L. Post treatment imaging in patients with local advanced cervical carcinoma. Front Oncol 2022; 12:1003930. [PMID: 36465360 PMCID: PMC9710522 DOI: 10.3389/fonc.2022.1003930] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/26/2022] [Indexed: 10/29/2023] Open
Abstract
Cervical cancer (CC) is the fourth leading cause of death in women worldwide and despite the introduction of screening programs about 30% of patients presents advanced disease at diagnosis and 30-50% of them relapse in the first 5-years after treatment. According to FIGO staging system 2018, stage IB3-IVA are classified as locally advanced cervical cancer (LACC); its correct therapeutic choice remains still controversial and includes neoadjuvant chemo-radiotherapy, external beam radiotherapy, brachytherapy, hysterectomy or a combination of these modalities. In this review we focus on the most appropriated therapeutic options for LACC and imaging protocols used for its correct follow-up. We explore the imaging findings after radiotherapy and surgery and discuss the role of imaging in evaluating the response rate to treatment, selecting patients for salvage surgery and evaluating recurrence of disease. We also introduce and evaluate the advances of the emerging imaging techniques mainly represented by spectroscopy, PET-MRI, and radiomics which have improved diagnostic accuracy and are approaching to future direction.
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Affiliation(s)
- S Ciulla
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
| | - V Celli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
| | - A A Aiello
- Department of Medical Sciences, University of Cagliari, Cagliari, Italy
| | - S Gigli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
| | - R Ninkova
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
| | - V Miceli
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
| | - G Ercolani
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
| | - M Dolciami
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
| | - P Ricci
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
| | - I Palaia
- Department of Maternal and Child Health and Urological Sciences, Sapienza, University of Rome, Rome, Italy
| | - C Catalano
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
| | - L Manganaro
- Department of Radiological, Oncological and Pathological Sciences, Sapienza, University of Rome, Rome, Italy
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Landsmann A, Ruppert C, Wieler J, Hejduk P, Ciritsis A, Borkowski K, Wurnig MC, Rossi C, Boss A. Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification. Eur Radiol Exp 2022; 6:30. [PMID: 35854186 PMCID: PMC9296720 DOI: 10.1186/s41747-022-00285-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 05/12/2022] [Indexed: 11/10/2022] Open
Abstract
Background We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT). Methods In this retrospective single-centre study, we analysed 10,000 images from 400 PC-BCT examinations of 200 patients. Images were categorised into four-level density scale (a–d) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria. After manual definition of representative regions of interest, 19 texture features (TFs) were calculated to analyse the voxel grey-level distribution in the included image area. ANOVA, cluster analysis, and multinomial logistic regression statistics were used. A human readout then was performed on a subset of 60 images to evaluate the reliability of the proposed feature set. Results Of the 19 TFs, 4 first-order features and 7 second-order features showed significant correlation with BD and were selected for further analysis. Multinomial logistic regression revealed an overall accuracy of 80% for BD assessment. The majority of TFs systematically increased or decreased with BD. Skewness (rho -0.81), as a first-order feature, and grey-level nonuniformity (GLN, -0.59), as a second-order feature, showed the strongest correlation with BD, independently of other TFs. Mean skewness and GLN decreased linearly from density a to d. Run-length nonuniformity (RLN), as a second-order feature, showed moderate correlation with BD, but resulted in redundant being correlated with GLN. All other TFs showed only weak correlation with BD (range -0.49 to 0.49, p < 0.001) and were neglected. Conclusion TA of PC-BCT images might be a useful approach to assess BD and may serve as an observer-independent tool.
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Affiliation(s)
- Anna Landsmann
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland.
| | - Carlotta Ruppert
- Institute of Computational Physics, Zurich University of Applied Sciences, Zurich, Switzerland
| | - Jann Wieler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Patryk Hejduk
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Alexander Ciritsis
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Karol Borkowski
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Moritz C Wurnig
- Institute of Diagnostic Radiology, Hospital Lachen AG, Lachen, Switzerland
| | - Cristina Rossi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Andreas Boss
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
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Zhang X, Zhao J, Zhang Q, Wang S, Zhang J, An J, Xie L, Yu X, Zhao X. MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy. Cancer Imaging 2022; 22:35. [PMID: 35842679 PMCID: PMC9287951 DOI: 10.1186/s40644-022-00474-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To investigate the magnetic resonance imaging (MRI)-based radiomics value in predicting the survival of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT). METHODS A total of 185 patients (training group: n = 128; testing group: n = 57) with LACSC treated with CCRT between January 2014 and December 2018 were retrospectively enrolled in this study. A total of 400 radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient map, arterial- and delayed-phase contrast-enhanced MRI. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression was applied to select radiomics features and clinical characteristics that could independently predict progression-free survival (PFS) and overall survival (OS). The predictive capability of the prediction model was evaluated using Harrell's C-index. Nomograms and calibration curves were then generated. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison. RESULTS The radiomics score achieved significantly better predictive performance for the estimation of PFS (C-index, 0.764 for training and 0.762 for testing) and OS (C-index, 0.793 for training and 0.750 for testing), compared with the 2018 FIGO staging system (C-index for PFS, 0.657 for training and 0.677 for testing; C-index for OS, 0.665 for training and 0.633 for testing) and clinical-predicting model (C-index for PFS, 0.731 for training and 0.725 for testing; C-index for OS, 0.708 for training and 0.693 for testing) (P < 0.05). The combined model constructed with T stage, lymph node metastasis position, and radiomics score achieved the best performance for the estimation of PFS (C-index, 0.792 for training and 0.809 for testing) and OS (C-index, 0.822 for training and 0.785 for testing), which were significantly higher than those of the radiomics score (P < 0.05). CONCLUSIONS The MRI-based radiomics score could provide effective information in predicting the PFS and OS in patients with LACSC treated with CCRT. The combined model (including MRI-based radiomics score and clinical characteristics) showed the best prediction performance.
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Affiliation(s)
- Xiaomiao Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jingwei Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Qi Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | | | - Jieying Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jusheng An
- Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lizhi Xie
- GE Healthcare, MR Research, Beijing, China
| | - Xiaoduo Yu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Wang W, Jiao Y, Zhang L, Fu C, Zhu X, Wang Q, Gu Y. Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage. Acta Radiol 2022; 63:847-856. [PMID: 33975448 DOI: 10.1177/02841851211014188] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND There are significant differences in outcomes for different histological subtypes of cervical cancer (CC). Yet, it is difficult to distinguish CC subtypes using non-invasive methods. PURPOSE To investigate whether multiparametric magnetic resonance imaging (MRI)-based radiomics analysis can differentiate CC subtypes and explore tumor heterogeneity. MATERIAL AND METHODS This study retrospectively analyzed 96 patients with CC (squamous cell carcinoma [SCC] = 50, adenocarcinoma [AC] = 46) who underwent pelvic MRI before surgery. Radiomics features were extracted from the tumor volumes on five sequences (sagittal T2-weighted imaging [T2SAG], transverse T2-weighted imaging [T2TRA], sagittal contrast-enhanced T1-weighted imaging [CESAG], transverse contrast-enhanced T1-weighted imaging [CETRA], and apparent diffusion coefficient [ADC]). Clustering and logistic regression were used to examine the distinguishing capabilities of radiomics features extracted from five different MR sequences. RESULTS Among the 105 extracted radiomics features, there were 51, 38, 37, and 2 features that showed intergroup differences for T2SAG, T2TRA, ADC, and CESAG, respectively (all P < 0.05). AC had greater textural heterogeneity than SCC (P < 0.05). Upon unsupervised clustering of significantly different features, T2SAG achieved the highest accuracy (0.844; sensitivity = 0.920; specificity = 0.761). The largest area under the curve (AUC) for classification ability was 0.86 for T2SAG. Hence, the radiomics model from five combined MR sequences (AUC = 0.89; accuracy = 0.81; sensitivity = 0.67; specificity = 0.94) exhibited better differentiation ability than any MR sequence alone. CONCLUSION Multiparametric MRI-based radiomics models may be a promising method to differentiate AC and SCC. AC showed more heterogeneous features than SCC.
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Affiliation(s)
- Wei Wang
- Department of Radiology, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China
| | - YiNing Jiao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, PR China
| | - LiChi Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, PR China
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, PR China
| | - XiaoLi Zhu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China
- Department of Pathology, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, PR China
| | - Qian Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, PR China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, PR China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China
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8
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Lu ZH, Xia KJ, Jiang H, Jiang JL, Wu M. Textural differences based on apparent diffusion coefficient maps for discriminating pT3 subclasses of rectal adenocarcinoma. World J Clin Cases 2021; 9:6987-6998. [PMID: 34540954 PMCID: PMC8409211 DOI: 10.12998/wjcc.v9.i24.6987] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 03/01/2021] [Accepted: 07/06/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The accuracy of discriminating pT3a from pT3b-c rectal cancer using high-resolution magnetic resonance imaging (MRI) remains unsatisfactory, although texture analysis (TA) could improve such discrimination.
AIM To investigate the value of TA on apparent diffusion coefficient (ADC) maps in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors.
METHODS This was a case-control study of 59 patients with pT3 rectal adenocarcinoma, who underwent diffusion-weighted imaging (DWI) between October 2016 and December 2018. The inclusion criteria were: (1) Proven pT3 rectal adenocarcinoma; (2) Primary MRI including high-resolution T2-weighted image (T2WI) and DWI; and (3) Availability of pathological reports for surgical specimens. The exclusion criteria were: (1) Poor image quality; (2) Preoperative chemoradiation therapy; and (3) A different pathological type. First-order (ADC values, skewness, kurtosis, and uniformity) and second-order (energy, entropy, inertia, and correlation) texture features were derived from whole-lesion ADC maps. Receiver operating characteristic curves were used to determine the diagnostic value for pT3b-c tumors.
RESULTS The final study population consisted of 59 patients (34 men and 25 women), with a median age of 66 years (range, 41-85 years). Thirty patients had pT3a, 24 had pT3b, and five had pT3c. Among the ADC first-order textural differences between pT3a and pT3b-c rectal adenocarcinomas, only skewness was significantly lower in the pT3a tumors than in pT3b-c tumors. Among the ADC second-order textural differences, energy and entropy were significantly different between pT3a and pT3b-c rectal adenocarcinomas. For differentiating pT3a rectal adenocarcinomas from pT3b-c tumors, the areas under the curves (AUCs) of skewness, energy, and entropy were 0.686, 0.657, and 0.747, respectively. Logistic regression analysis of all three features yielded a greater AUC (0.775) in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors (69.0% sensitivity and 83.3% specificity).
CONCLUSION TA features derived from ADC maps might potentially differentiate pT3a rectal adenocarcinomas from pT3b-c tumors.
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Affiliation(s)
- Zhi-Hua Lu
- Department of Radiology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Changshu 215500, Jiangsu Province, China
| | - Kai-Jian Xia
- Department of Information, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Changshu 215500, Jiangsu Province, China
| | - Heng Jiang
- Department of Radiology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Changshu 215500, Jiangsu Province, China
| | - Jian-Long Jiang
- Department of Surgery, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Changshu 215500, Jiangsu Province, China
| | - Mei Wu
- Department of Pathology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Changshu 215500, Jiangsu Province, China
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9
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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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10
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Mi H, Yuan M, Suo S, Cheng J, Li S, Duan S, Lu Q. Impact of different scanners and acquisition parameters on robustness of MR radiomics features based on women's cervix. Sci Rep 2020; 10:20407. [PMID: 33230228 PMCID: PMC7684312 DOI: 10.1038/s41598-020-76989-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 10/16/2020] [Indexed: 12/12/2022] Open
Abstract
MR Radiomics based on cervical lesions from one single scanner has achieved promising results. However, it is a challenge to achieve clinical translation. Considering multi-scanners and non-uniform scanning parameters from different centers in a real-world medical scenario, we should first identify the influence of such conditions on the robustness of MR radiomics features (RFs) based on the female cervix. In this study, 9 healthy female volunteers were enrolled and 3 kiwis were selected as references. Each of them underwent T2 weighted imaging in three different 3.0-T MR scanners with uniform acquisition parameters, and in one MR scanner with various scanning parameters. A total of 396 RFs were extracted from their images with and without decile intensity normalization. The RFs’ reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Representative features were selected using the hierarchical cluster analysis and their discrimination abilities were estimated by ROC analysis through retrospective comparison with the junctional zone and the outer muscular layer of healthy cervix in patients (n = 58) with leiomyoma. This study showed that only a few RFs were robust across different MR scanners and acquisition parameters based on females’ cervix, which might be improved by decile intensity normalization method.
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Affiliation(s)
- Honglan Mi
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China
| | - Mingyuan Yuan
- Department of Radiology, Affiliated Zhoupu Hospital, Shanghai University of Medicine & Health Sciences College, 1500 Zhouyuan Road, PongDong New District, Shanghai, 201318, China
| | - Shiteng Suo
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China
| | - Jiejun Cheng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China
| | - Suqin Li
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China
| | - Shaofeng Duan
- GE Healthcare China, Pudong new town, No1, Huatuo road, Shanghai, 210000, China
| | - Qing Lu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Rd, Shanghai, 200127, China.
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11
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Hou L, Zhou W, Ren J, Du X, Xin L, Zhao X, Cui Y, Zhang R. Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer. Front Oncol 2020; 10:1393. [PMID: 32974143 PMCID: PMC7468409 DOI: 10.3389/fonc.2020.01393] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 07/02/2020] [Indexed: 01/08/2023] Open
Abstract
Objective: To develop and validate a radiomics predictive model based on multiparameter MR imaging features and clinical features to predict lymph node metastasis (LNM) in patients with cervical cancer. Material and Methods: A total of 168 consecutive patients with cervical cancer from two centers were enrolled in our retrospective study. A total of 3,930 imaging features were extracted from T2-weighted (T2W), ADC, and contrast-enhanced T1-weighted (cT1W) images for each patient. Four-step procedures, mainly minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) regression, were applied for feature selection and radiomics signature building in the training set from center I (n = 115). Combining clinical risk factors, a radiomics nomogram was then constructed. The models were then validated in the external validation set comprising 53 patients from center II. The predictive performance was determined by its calibration, discrimination, and clinical usefulness. Results: The radiomics signature derived from the combination of T2W, ADC, and cT1W images, composed of six LN-status-related features, was significantly associated with LNM and showed better predictive performance than signatures derived from either of them alone in both sets. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN-negative subgroup, with AUC of 0.825 (95% CI: 0.732–0.919). The radiomics nomogram that incorporated radiomics signature and MRI-reported LN status also showed good calibration and discrimination in both sets, with AUCs of 0.865 (95% CI: 0.794–0.936) and 0.861 (95% CI: 0.733–0.990), respectively. Decision curve analysis confirmed its clinical usefulness. Conclusion: The proposed MRI-based radiomics nomogram has good performance for predicting LN metastasis in cervical cancer and may be useful for improving clinical decision making.
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Affiliation(s)
- Lina Hou
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Wei Zhou
- Department of Radiology, Huzhou Central Hospital, Affiliated to Huzhou University, Huzhou, China
| | | | - Xiaosong Du
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Lei Xin
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Xin Zhao
- Department of Gynecology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Ruiping Zhang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
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12
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Jin X, Ai Y, Zhang J, Zhu H, Jin J, Teng Y, Chen B, Xie C. Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images. Eur Radiol 2020; 30:4117-4124. [PMID: 32078013 DOI: 10.1007/s00330-020-06692-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/18/2019] [Accepted: 01/30/2020] [Indexed: 12/27/2022]
Abstract
OBJECTIVE To investigate the feasibility of a noninvasive detection of lymph node metastasis (LNM) for early-stage cervical cancer (ECC) patients with radiomics methods based on the textural features from ultrasound images. METHODS One hundred seventy-two ECC patients between January 2014 and September 2018 with pathologically confirmed lymph node status (LNS) and preoperative ultrasound images were retrospectively reviewed. Regions of interest (ROIs) were delineated by a senior radiologist in the ultrasound images. LIFEx was applied to extract textural features for radiomics study. Least absolute shrinkage and selection operator (LASSO) regression was applied for dimension reduction and for selection of key features. A multivariable logistic regression analysis was adopted to build the radiomics signature. The Mann-Whitney U test was applied to investigate the correlation between radiomics and LNS for both training and validation cohorts. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the radiomics prediction models. RESULTS A total of 152 radiomics features were extracted from ultrasound images, in which 6 features were significantly associated with LNS (p < 0.05). The radiomics signatures demonstrated a good discrimination between patients with LNM and non-LNM groups. The best radiomics performance model achieved an area under the curve (AUC) of 0.79 (95% confidence interval (CI), 0.71-0.88) in the training cohort and 0.77 (95% CI, 0.65-0.88) in the validation cohort. CONCLUSIONS The feasibility of radiomics features from ultrasound images for the prediction of LNM in ECC was investigated. This noninvasive prediction method may be used to facilitate preoperative identification of LNS in patients with ECC. KEY POINTS • Few studied had investigated the feasibility of radiomics based on ultrasound images for cervical cancer, even though it is the most common practice for gynecological cancer diagnosis and treatment. • The radiomics signatures based on ultrasound images demonstrated a good discrimination between patients with and without lymph node metastasis with an area under the curve (AUC) of 0.79 and 0.77 in the training and validation cohorts, respectively. • The radiomics model based on preoperative ultrasound images has the potential ability to predict lymph node status noninvasively in patients with early-state cervical cancer, so as to reduce the impact of invasive examination and to optimize the treatment choices.
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Affiliation(s)
- Xiance Jin
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Yao Ai
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Ji Zhang
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Haiyan Zhu
- Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
- Department of Gynecology, Shanghai First Maternal and Infant Hospital, Tongji University School of Medicine, Shanghai, 200126, People's Republic of China
| | - Juebin Jin
- Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Yinyan Teng
- Department of Ultrasound imaging, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China
| | - Bin Chen
- Department of Ultrasound imaging, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China.
| | - Congying Xie
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Wenzhou, 325000, People's Republic of China.
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13
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Yamada I, Oshima N, Miyasaka N, Wakana K, Wakabayashi A, Sakamoto J, Saida Y, Tateishi U, Kobayashi D. Texture Analysis of Apparent Diffusion Coefficient Maps in Cervical Carcinoma: Correlation with Histopathologic Findings and Prognosis. Radiol Imaging Cancer 2020; 2:e190085. [PMID: 33778713 DOI: 10.1148/rycan.2020190085] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 02/17/2020] [Accepted: 02/28/2020] [Indexed: 12/29/2022]
Abstract
Purpose To determine the feasibility of texture analysis of apparent diffusion coefficient (ADC) maps and to assess the performance of texture analysis and ADC to predict histologic grade, parametrial invasion, lymph node metastasis, International Federation of Gynecology and Obstetrics (FIGO) stage, recurrence, and recurrence-free survival (RFS) in patients with cervical carcinoma. Materials and Methods This retrospective study included 58 patients with cervical carcinoma who were examined with a 1.5-T MRI system and diffusion-weighted imaging with b values of 0 and 1000 sec/mm2. Software with volumes of interest on ADC maps was used to extract 45 texture features, including higher-order texture features. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic performance of ADC map random forest models and of ADC values. Dunnett test, Spearman rank correlation coefficient, Kaplan-Meier analyses, log-rank test, and Cox proportional hazards regression analyses were also used for statistical analyses. Results The ADC map random forest models showed a significantly larger area under the ROC curve (AUC) than the AUC of ADC values for predicting high-grade cervical carcinoma (P = .0036), but not for parametrial invasion, lymph node metastasis, stages III-IV, and recurrence (P = .0602, .3176, .0924, and .5633, respectively). The random forest models predicted that the mean RFS rates were significantly shorter for high-grade cervical carcinomas, parametrial invasion, lymph node metastasis, stages III-IV, and recurrence (P = .0405, < .0001, .0344, .0001, and .0015, respectively); the random forest models for parametrial invasion and stages III-IV were more useful than ADC values (P = .0018) for predicting RFS. Conclusion The ADC map random forest models were more useful for noninvasively evaluating histologic grade, parametrial invasion, lymph node metastasis, FIGO stage, and recurrence and for predicting RFS in patients with cervical carcinoma than were ADC values.Keywords: Comparative Studies, Genital/Reproductive, MR-Diffusion Weighted Imaging, MR-Imaging, Neoplasms-Primary, Pathology, Pelvis, Tissue Characterization, UterusSupplemental material is available for this article.© RSNA, 2020See also the commentary by Reinhold and Nougaret in this issue.
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Affiliation(s)
- Ichiro Yamada
- Departments of Diagnostic Radiology and Nuclear Medicine (I.Y., Y.S., U.T.), Comprehensive Reproductive Medicine (N.O., N.M., K.W., A.W.), Oral and Maxillofacial Radiology (J.S.), and Human Pathology (D.K.), Graduate School, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Noriko Oshima
- Departments of Diagnostic Radiology and Nuclear Medicine (I.Y., Y.S., U.T.), Comprehensive Reproductive Medicine (N.O., N.M., K.W., A.W.), Oral and Maxillofacial Radiology (J.S.), and Human Pathology (D.K.), Graduate School, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Naoyuki Miyasaka
- Departments of Diagnostic Radiology and Nuclear Medicine (I.Y., Y.S., U.T.), Comprehensive Reproductive Medicine (N.O., N.M., K.W., A.W.), Oral and Maxillofacial Radiology (J.S.), and Human Pathology (D.K.), Graduate School, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Kimio Wakana
- Departments of Diagnostic Radiology and Nuclear Medicine (I.Y., Y.S., U.T.), Comprehensive Reproductive Medicine (N.O., N.M., K.W., A.W.), Oral and Maxillofacial Radiology (J.S.), and Human Pathology (D.K.), Graduate School, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Akira Wakabayashi
- Departments of Diagnostic Radiology and Nuclear Medicine (I.Y., Y.S., U.T.), Comprehensive Reproductive Medicine (N.O., N.M., K.W., A.W.), Oral and Maxillofacial Radiology (J.S.), and Human Pathology (D.K.), Graduate School, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Junichiro Sakamoto
- Departments of Diagnostic Radiology and Nuclear Medicine (I.Y., Y.S., U.T.), Comprehensive Reproductive Medicine (N.O., N.M., K.W., A.W.), Oral and Maxillofacial Radiology (J.S.), and Human Pathology (D.K.), Graduate School, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Yukihisa Saida
- Departments of Diagnostic Radiology and Nuclear Medicine (I.Y., Y.S., U.T.), Comprehensive Reproductive Medicine (N.O., N.M., K.W., A.W.), Oral and Maxillofacial Radiology (J.S.), and Human Pathology (D.K.), Graduate School, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Ukihide Tateishi
- Departments of Diagnostic Radiology and Nuclear Medicine (I.Y., Y.S., U.T.), Comprehensive Reproductive Medicine (N.O., N.M., K.W., A.W.), Oral and Maxillofacial Radiology (J.S.), and Human Pathology (D.K.), Graduate School, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
| | - Daisuke Kobayashi
- Departments of Diagnostic Radiology and Nuclear Medicine (I.Y., Y.S., U.T.), Comprehensive Reproductive Medicine (N.O., N.M., K.W., A.W.), Oral and Maxillofacial Radiology (J.S.), and Human Pathology (D.K.), Graduate School, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan
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14
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MRI texture features differentiate clinicopathological characteristics of cervical carcinoma. Eur Radiol 2020; 30:5384-5391. [PMID: 32382845 DOI: 10.1007/s00330-020-06913-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 04/23/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC). METHODS Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC. RESULTS Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively. CONCLUSIONS Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC. KEY POINTS • First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.
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15
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Yin JD, Song LR, Lu HC, Zheng X. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps. World J Gastroenterol 2020; 26:2082-2096. [PMID: 32536776 PMCID: PMC7267694 DOI: 10.3748/wjg.v26.i17.2082] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/26/2020] [Accepted: 04/14/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning. It has not been extensively investigated whether texture features derived from diffusion-weighted imaging (DWI) images and apparent diffusion coefficient (ADC) maps are associated with the extent of local invasion (pathological stage T1-2 vs T3-4) and nodal involvement (pathological stage N0 vs N1-2) in rectal cancer.
AIM To predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.
METHODS One hundred and fifteen patients with pathologically proven rectal cancer, who underwent preoperative magnetic resonance imaging, including DWI, were enrolled, retrospectively. The ADC measurements (ADCmean, ADCmin, ADCmax) as well as texture features, including the gray level co-occurrence matrix parameters, the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI (b = 0 and b = 1000) images and the ADC maps. Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis. Multivariate logistic regression analysis was conducted to establish the models. The predictive performance was validated by receiver operating characteristic curve analysis.
RESULTS Dissimilarity, sum average, information correlation and run-length nonuniformity from DWIb=0 images, gray level nonuniformity, run percentage and run-length nonuniformity from DWIb=1000 images, and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion (stage T3-4). The area under the operating characteristic curve of the model reached 0.793 with a sensitivity of 78.57% and a specificity of 74.19%. Sum average, gray level nonuniformity and the horizontal components of symlet transform (SymletH) from DWIb=0 images, sum average, information correlation, long run low gray level emphasis and SymletH from DWIb=1000 images, and ADCmax, ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement. The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77% and a specificity of 68.25%.
CONCLUSION Texture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.
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Affiliation(s)
- Jian-Dong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110003, Liaoning Province, China
| | - Li-Rong Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110003, Liaoning Province, China
| | - He-Cheng Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110036, Liaoning Province, China
| | - Xu Zheng
- Department of Clinical Oncology, Shengjing Hospital of China Medical University, Shenyang 110011, Liaoning Province, China
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16
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Park SH, Hahm MH, Bae BK, Chong GO, Jeong SY, Na S, Jeong S, Kim JC. Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis. Radiat Oncol 2020; 15:86. [PMID: 32312283 PMCID: PMC7171757 DOI: 10.1186/s13014-020-01502-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 02/19/2020] [Indexed: 01/08/2023] Open
Abstract
Background Current chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical cancer patients. Methods We included 93 node-positive cervical cancer patients treated with definitive chemoradiotherapy at our institution between 2006 and 2017. The median follow-up period was 38 months (range, 5–128). Primary tumor and involved lymph node were manually segmented on axial gadolinium-enhanced T1-weighted images as well as T2-weighted images and saved as 3-dimensional regions of interest (ROI). After the segmentation, imaging features related to histogram, shape, and texture were extracted from each ROI. Using these features, random survival forest (RSF) models were built to predict local control (LC), regional control (RC), distant metastasis-free survival (DMFS), and overall survival (OS) in the training dataset (n = 62). The generated models were then tested in the validation dataset (n = 31). Results For predicting LC, models generated from primary tumor imaging features showed better predictive performance (C-index, 0.72) than those from lymph node features (C-index, 0.62). In contrast, models from lymph nodes showed superior performance for predicting RC, DMFS, and OS compared to models of the primary tumor. According to the 3-year time-dependent receiver operating characteristic analysis of LC, RC, DMFS, and OS prediction, the respective area under the curve values for the predicted risk of the models generated from the training dataset were 0.634, 0.796, 0.733, and 0.749 in the validation dataset. Conclusions Our results suggest that tumor and lymph node imaging features may play complementary roles for predicting clinical outcomes in node-positive cervical cancer.
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Affiliation(s)
- Shin-Hyung Park
- Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
| | - Myong Hun Hahm
- Department of Radiology, School of Medicine, Kyungpook National University, Daegu, Korea, Republic of Korea
| | - Bong Kyung Bae
- Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Gun Oh Chong
- Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.,Department of Obstetrics and Gynecology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.,Molecular Diagnostics and Imaging Center, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Shin Young Jeong
- Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sungdae Na
- Department of Biomedical Engineering Center, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Sungmoon Jeong
- Bio-Medical Research Institute, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.,Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Jae-Chul Kim
- Department of Radiation Oncology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
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17
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Perucho JAU, Chiu KWH, Wong EMF, Tse KY, Chu MMY, Chan LWC, Pang H, Khong PL, Lee EYP. Diffusion-weighted magnetic resonance imaging of primary cervical cancer in the detection of sub-centimetre metastatic lymph nodes. Cancer Imaging 2020; 20:27. [PMID: 32252829 PMCID: PMC7137185 DOI: 10.1186/s40644-020-00303-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 03/20/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) has limited accuracy in detecting pelvic lymph node (PLN) metastasis. This study aimed to examine the use of intravoxel incoherent motion (IVIM) in classifying pelvic lymph node (PLN) involvement in cervical cancer patients. METHODS Fifty cervical cancer patients with pre-treatment magnetic resonance imaging (MRI) were examined for PLN involvement by one subspecialist and one non-subspecialist radiologist. PLN status was confirmed by positron emission tomography or histology. The tumours were then segmented by both radiologists. Kruskal-Wallis tests were used to test for differences between diffusion tumour volume (DTV), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), and perfusion fraction (f) in patients with no malignant PLN involvement, those with sub-centimetre and size-significant PLN metastases. These parameters were then considered as classifiers for PLN involvement, and were compared with the accuracies of radiologists. RESULTS Twenty-one patients had PLN involvement of which 10 had sub-centimetre metastatic PLNs. DTV increased (p = 0.013) while ADC (p = 0.015), and f (p = 0.006) decreased as the nodal status progressed from no malignant involvement to sub-centimetre and then size-significant PLN metastases. In determining PLN involvement, a classification model (DTV + f) had similar accuracies (80%) as the non-subspecialist (76%; p = 0.73) and subspecialist (90%; p = 0.31). However, in identifying patients with sub-centimetre PLN metastasis, the model had higher accuracy (90%) than the non-subspecialist (30%; p = 0.01) but had similar accuracy with the subspecialist (90%, p = 1.00). Interobserver variability in tumour delineation did not significantly affect the performance of the classification model. CONCLUSION IVIM is useful in determining PLN involvement but the added value decreases with reader experience.
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Affiliation(s)
- Jose Angelo Udal Perucho
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pok Fu Lam Road, Pok Fu Lam, Hong Kong
| | - Keith Wan Hang Chiu
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pok Fu Lam Road, Pok Fu Lam, Hong Kong
| | - Esther Man Fung Wong
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, 3 Lok Man Road, Chai Wan, Hong Kong
| | - Ka Yu Tse
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 6/F, Professorial Block, Queen Mary Hospital, Pok Fu Lam Road, Pok Fu Lam, Hong Kong
| | - Mandy Man Yee Chu
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 6/F, Professorial Block, Queen Mary Hospital, Pok Fu Lam Road, Pok Fu Lam, Hong Kong
| | - Lawrence Wing Chi Chan
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Room Y934, 9/F, Lee Shau Kee Building, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Herbert Pang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, G/F, Patrick Manson Building (North Wing), 7 Sassoon Road, Pok Fu Lam, Hong Kong
| | - Pek-Lan Khong
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pok Fu Lam Road, Pok Fu Lam, Hong Kong
| | - Elaine Yuen Phin Lee
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pok Fu Lam Road, Pok Fu Lam, Hong Kong
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He Y, Rong Y, Chen H, Zhang Z, Qiu J, Zheng L, Benedict S, Niu X, Pan N, Liu Y, Yuan Z. Impact of different b-value combinations on radiomics features of apparent diffusion coefficient in cervical cancer. Acta Radiol 2020; 61:568-576. [PMID: 31466457 DOI: 10.1177/0284185119870157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background The impact of variable b-value combinations on apparent diffusion coefficient (ADC)-based radiomics features has not been fully addressed in literature. Purpose To investigate the correlation between radiomics features extracted from ADC maps and various b-value combinations in cervical cancer. Material and Methods Diffusion-weighted images (b-values: 0, 600, 800, and 1000 s/mm2) of 20 patients with cervical cancer were included. Tumors were identified with the largest transversal cross-section and manually segmented by radiologist. For each b-value combination, 92 radiomics features were extracted and coefficient of variance (CV) was used to evaluate the robustness of radiomics features with different b-value combinations. Features with CV > 5% were normalized by the mean feature variation across the group. Results Out of a total of 92 radiomics features, 18 were classified as robust features with CV ≤5%. Among the rest (CV > 5%), 11, 23, and 40 features demonstrated 5%< CV ≤10%, 10%< CV ≤20%, and CV > 20%, respectively. A subset of features in each category (CV > 5%) showed strong correlation with the b-value combination variation, including 44% (7/16) features in gray level co-occurrence matrix, 62% (8/13) features in gray level dependence matrix, 64% (9/14) features in first order, 50% (8/16) features in gray level run length matrix, 57% (8/14) features in gray level size matrix, and 20% (1/5) features in neighborhood gray-tone difference matrix. Conclusions Variations in b-value combinations demonstrated impact on radiomics features extracted from ADC maps for cervical cancer. The radiomics features with CV <5% can be considered as robust features and are recommended to be used in multicenter radiomics studies.
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Affiliation(s)
- Yaoyao He
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
- Medical Engineering and Technology Center, Taishan Medical University, Taian, PR China
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, USA
| | - Hao Chen
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zhaoxi Zhang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Jianfeng Qiu
- Medical Engineering and Technology Center, Taishan Medical University, Taian, PR China
| | - Lili Zheng
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Stanley Benedict
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, USA
| | - Xiaohui Niu
- College of Informatics, Huazhong Agricultural University, Wuhan, PR China
| | - Ning Pan
- College of Biomedical Engineering, South Central University for Nationalities, Wuhan, PR China
- Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, Wuhan, PR China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
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Gao J, Huang X, Meng H, Zhang M, Zhang X, Lin X, Li B. Performance of Multiparametric Functional Imaging and Texture Analysis in Predicting Synchronous Metastatic Disease in Pancreatic Ductal Adenocarcinoma Patients by Hybrid PET/MR: Initial Experience. Front Oncol 2020; 10:198. [PMID: 32158690 PMCID: PMC7052324 DOI: 10.3389/fonc.2020.00198] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 02/05/2020] [Indexed: 12/16/2022] Open
Abstract
Objectives: To assess the imaging biomarkers of glucose metabolic activity and diffusion-weighted imaging (DWI) derived from pretreatment integrated 18F-fluorodeoxyglucose positron emission tomography-magnetic resonance (18F-FDG PET/MR) imaging as potential predictive factors of metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Patients and Methods: We retrospectively included 17 consecutive patients with pathologically confirmed PDAC by pretreatment 18F-FDG PET/MR. The study subjects were divided into a non-metastatic group (M0, six cases) and a metastatic group (M1, 11 cases). The 18F-FDG PET/MR images were reviewed independently by two board certificated nuclear medicine physicians and one radiologist. Conventional characteristics and quantitative parameters from both PET and apparent diffusion coefficient (ADC) were assessed. The texture features were extracted from LIFEx packages (www.lifexsoft.org), and a 3D tumor volume of interest was manually drawn on fused PET/ADC images. Chi-square tests, independent-samples t-tests and Mann-Whitney U-tests were used to compare the differences in single parameters between the two groups. A logistic regression analysis was performed to determine independent predictors. A receiver operating characteristic (ROC) curve analysis was performed to assess the discriminatory power of the selected parameters. Correlations between metabolic parameters and ADC features were calculated with Spearman's rank correlation coefficient test. Results: For conventional parameters, univariable analysis demonstrated that the M1 group had a significantly larger size and a higher peak of standardized uptake value (SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) than those of the M0 group (p < 0.05 for all). TLG remained significant predictor in the multivariable analysis, but there were no significant differences for the area under the ROC curve (AUC) among the four conventional features in differential diagnoses (p > 0.05 for all). For the texture features, there were four features from the PET image and 13 from the ADC map that showed significant differences between the two groups. Multivariate analysis indicated that one feature from PET and three from the ADC were significant predictors. TLG was associated with ADC-GLRLM_GLNU (r = 0.659), ADC-GLRLM_LRHGE (r = 0.762), and PET-GLRLM_LRHGE (r = 0.806). Conclusions: Multiple parameters and texture features of primary tumors from 18F-FDG PET/MR images maybe reliable biomarkers to predict synchronous metastatic disease for the pretreatment PDAC.
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Affiliation(s)
- Jing Gao
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyun Huang
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongping Meng
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Miao Zhang
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaozhe Zhang
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaozhu Lin
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Biao Li
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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20
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Wang T, Gao T, Guo H, Wang Y, Zhou X, Tian J, Huang L, Zhang M. Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram. Eur Radiol 2020; 30:3585-3593. [PMID: 32065284 DOI: 10.1007/s00330-019-06655-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 12/02/2019] [Accepted: 12/20/2019] [Indexed: 01/08/2023]
Abstract
PURPOSE To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC). MATERIALS AND METHODS All 137 patients with ECC (FIGO stages IB-IIA) underwent T2WI and DWI scans before radical hysterectomy surgery. The radiomics signatures were calculated with the radiomics features which were extracted from T2WI and DWI and selected by the least absolute shrinkage and selection operation regression. The support vector machine (SVM) models were built using radiomics signatures derived from T2WI and joint T2WI and DWI respectively to evaluate the performance of radiomics signatures for distinguishing patients with PMI. A radiomics nomogram was drawn based on the radiomics signatures with a better performance, patient's age, and pathological grade; its discrimination and calibration performances were estimated. RESULTS For T2WI and joint T2WI and DWI, the radiomics signatures yielded an AUC of 0.797 (95% CI, 0.682-0.911) vs 0.946 (95% CI, 0.899-0.994), and 0.780 (95% CI, 0.641-0.920) vs 0.921 (95% CI, 0.832-1) respectively in the primary and validation cohorts. The radiomics nomogram, integrating the radiomics signatures from joint T2WI and DWI, patient's age, and pathological grade, showed excellent discrimination, with C-index values of 0.969 (95% CI, 0.933-1) and 0.941 (95% CI, 0.868-1) in the primary and validation cohorts, respectively. The calibration curve showed a good agreement. CONCLUSIONS The radiomics nomogram performed well for the preoperative prediction of PMI in patients with ECC and may be used as a supplementary tool to provide individualized treatment plans for patients with ECC. KEY POINTS • No previously reported study that has utilized radiomics nomogram to preoperatively predict PMI for patients with ECC. • Radiomics model involves radiomics features extracted from joint T2WI and DWI which characterize the heterogeneity between tumors in patients with ECC. • Radiomics nomogram can assist clinicians with individualized treatment decision-making for patients with ECC.
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Affiliation(s)
- Tao Wang
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, No.277, West Yanta Road, Xi'an, 710061, Shaanxi, People's Republic of China.,Department of Radiology, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, People's Republic of China
| | - Tingting Gao
- School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, People's Republic of China
| | - Hua Guo
- Center Laboratory, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, People's Republic of China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, People's Republic of China
| | - Xiaobo Zhou
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, 100080, People's Republic of China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, People's Republic of China.
| | - Ming Zhang
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, No.277, West Yanta Road, Xi'an, 710061, Shaanxi, People's Republic of China.
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21
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Perucho JAU, Chang HCC, Vardhanabhuti V, Wang M, Becker AS, Wurnig MC, Lee EYP. B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer. Korean J Radiol 2020; 21:218-227. [PMID: 31997597 PMCID: PMC6992446 DOI: 10.3348/kjr.2019.0232] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 10/30/2019] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE This study aimed to find the optimal number of b-values for intravoxel incoherent motion (IVIM) imaging analysis, using simulated and in vivo data from cervical cancer patients. MATERIALS AND METHODS Simulated data were generated using literature pooled means, which served as reference values for simulations. In vivo data from 100 treatment-naïve cervical cancer patients with IVIM imaging (13 b-values, scan time, 436 seconds) were retrospectively reviewed. A stepwise b-value fitting algorithm calculated optimal thresholds. Feed forward selection determined the optimal subsampled b-value distribution for biexponential IVIM fitting, and simplified IVIM modeling using monoexponential fitting was attempted. IVIM parameters computed using all b-values served as reference values for in vivo data. RESULTS In simulations, parameters were accurately estimated with six b-values, or three b-values for simplified IVIM, respectively. In vivo data showed that the optimal threshold was 40 s/mm² for patients with squamous cell carcinoma and a subsampled acquisition of six b-values (scan time, 198 seconds) estimated parameters were not significantly different from reference parameters (individual parameter error rates of less than 5%). In patients with adenocarcinoma, the optimal threshold was 100 s/mm², but an optimal subsample could not be identified. Irrespective of the histological subtype, only three b-values were needed for simplified IVIM, but these parameters did not retain their discriminative ability. CONCLUSION Subsampling of six b-values halved the IVIM scan time without significant losses in accuracy and discriminative ability. Simplified IVIM is possible with only three b-values, at the risk of losing diagnostic information.
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Affiliation(s)
| | | | | | - Mandi Wang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong
| | - Anton Sebastian Becker
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Switzerland
| | - Moritz Christoph Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Switzerland
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22
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Wu Y, Jiang JH, Chen L, Lu JY, Ge JJ, Liu FT, Yu JT, Lin W, Zuo CT, Wang J. Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:773. [PMID: 32042789 DOI: 10.21037/atm.2019.11.26] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Parkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract high-order features by using radiomics approach and achieve acceptable diagnosis accuracy in PD. Methods In this retrospective multicohort study, we collected 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and clinical scale [the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn & Yahr scale (H&Y)] from two cohorts. One cohort from Huashan Hospital had 91 normal controls (NC) and 91 PD patients (UPDRS: 22.7±11.7, H&Y: 1.8±0.8), and the other cohort from Wuxi 904 Hospital had 26 NC and 22 PD patients (UPDRS: 20.9±11.6, H&Y: 1.7±0.9). The Huashan cohort was used as the training and test sets by 5-fold cross-validation and the Wuxi cohort was used as another separate test set. After identifying regions of interests (ROIs) based on the atlas-based method, radiomic features were extracted and selected by using autocorrelation and fisher score algorithm. A support vector machine (SVM) was trained to classify PD and NC based on selected radiomic features. In the comparative experiment, we compared our method with the traditional voxel values method. To guarantee the robustness, above processes were repeated in 500 times. Results Twenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively. Conclusions This study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.
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Affiliation(s)
- Yue Wu
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
| | - Jie-Hui Jiang
- Department of Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
| | - Li Chen
- Department of Medical Ultrasound, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jia-Ying Lu
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jing-Jie Ge
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Feng-Tao Liu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jin-Tai Yu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wei Lin
- Department of Neurosurgery, 904 Hospital of PLA, Anhui Medical University, Wuxi 214000, China
| | - Chuan-Tao Zuo
- Department of PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jian Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
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23
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Lu Z, Wang L, Xia K, Jiang H, Weng X, Jiang J, Wu M. Prediction of Clinical Pathologic Prognostic Factors for Rectal Adenocarcinoma: Volumetric Texture Analysis Based on Apparent Diffusion Coefficient Maps. J Med Syst 2019; 43:331. [DOI: 10.1007/s10916-019-1464-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 09/26/2019] [Indexed: 12/14/2022]
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24
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Marcon M, Ciritsis A, Rossi C, Becker AS, Berger N, Wurnig MC, Wagner MW, Frauenfelder T, Boss A. Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study. Eur Radiol Exp 2019; 3:44. [PMID: 31676937 PMCID: PMC6825080 DOI: 10.1186/s41747-019-0121-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 08/28/2019] [Indexed: 12/31/2022] Open
Abstract
Background Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification. Methods This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions. Results Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii). Conclusions TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF. Electronic supplementary material The online version of this article (10.1186/s41747-019-0121-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Magda Marcon
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
| | - Alexander Ciritsis
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Cristina Rossi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Anton S Becker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Nicole Berger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Moritz C Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Matthias W Wagner
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Andreas Boss
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
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Texture Analysis of Multi-Shot Echo-planar Diffusion-Weighted Imaging in Head and Neck Squamous Cell Carcinoma: The Diagnostic Value for Nodal Metastasis. J Clin Med 2019; 8:jcm8111767. [PMID: 31652840 PMCID: PMC6912832 DOI: 10.3390/jcm8111767] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/13/2019] [Accepted: 10/22/2019] [Indexed: 12/28/2022] Open
Abstract
Accurate assessment of nodal metastasis in head and neck squamous cell carcinoma (SCC) is important, and diffusion-weighted imaging (DWI) has emerged as a potential technique in differentiating benign from malignant lymph nodes (LNs). This study aims to evaluate the diagnostic performance of texture analysis using apparent diffusion coefficient (ADC) data of multi-shot echo-planar imaging-based DWI (msEPI-DWI) in predicting metastatic LNs of head and neck SCC. 36 patients with pathologically proven head and neck SCC were included in this study. A total of 204 MRI-detected LNs, including 176 subcentimeter-sized LNs, were assigned to metastatic or benign groups. Texture features of LNs were compared using independent t-test. Hierarchical cluster analysis was performed to exclude redundant features. Multivariate logistic regression and receiver operating characteristic analysis were performed to assess the diagnostic performance. The discriminative texture features for predicting metastatic LNs were complexity, energy and roundness. Areas under the curves (AUCs) for diagnosing metastasis in all/subcentimeter-sized LNs were 0.829/0.767 using complexity, 0.699/0.685 using energy and 0.671/0.638 using roundness, respectively. The combination of three features resulted in higher AUC values of 0.836/0.781. In conclusion, texture analysis of ADC data using msEPI-DWI could be a useful tool for nodal staging in head and neck SCC.
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Haldorsen IS, Lura N, Blaakær J, Fischerova D, Werner HMJ. What Is the Role of Imaging at Primary Diagnostic Work-Up in Uterine Cervical Cancer? Curr Oncol Rep 2019; 21:77. [PMID: 31359169 PMCID: PMC6663927 DOI: 10.1007/s11912-019-0824-0] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE OF REVIEW For uterine cervical cancer, the recently revised International Federation of Gynecology and Obstetrics (FIGO) staging system (2018) incorporates imaging and pathology assessments in its staging. In this review we summarize the reported staging performances of conventional and novel imaging methods and provide an overview of promising novel imaging methods relevant for cervical cancer patient care. RECENT FINDINGS Diagnostic imaging during the primary diagnostic work-up is recommended to better assess tumor extent and metastatic disease and is now reflected in the 2018 FIGO stages 3C1 and 3C2 (positive pelvic and/or paraaortic lymph nodes). For pretreatment local staging, imaging by transvaginal or transrectal ultrasound (TVS, TRS) and/or magnetic resonance imaging (MRI) is instrumental to define pelvic tumor extent, including a more accurate assessment of tumor size, stromal invasion depth, and parametrial invasion. In locally advanced cervical cancer, positron emission tomography-computed tomography (PET-CT) or computed tomography (CT) is recommended, since the identification of metastatic lymph nodes and distant metastases has therapeutic consequences. Furthermore, novel imaging techniques offer visualization of microstructural and functional tumor characteristics, reportedly linked to clinical phenotype, thus with a potential for further improving risk stratification and individualization of treatment. Diagnostic imaging by MRI/TVS/TRS and PET-CT/CT is instrumental for pretreatment staging in uterine cervical cancer and guides optimal treatment strategy. Novel imaging techniques may also provide functional biomarkers with potential relevance for developing more targeted treatment strategies in cervical cancer.
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Affiliation(s)
- Ingfrid S Haldorsen
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Liesvei 65, Postbox 7800, 5021, Bergen, Norway.
- Section for Radiology, Department of Clinical Medicine, University of Bergen, 5020, Bergen, Norway.
| | - Njål Lura
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Liesvei 65, Postbox 7800, 5021, Bergen, Norway
| | - Jan Blaakær
- Department of Obstetrics and Gynaecology, Odense University Hospital, Odense, Denmark
| | - Daniela Fischerova
- Gynecological Oncology Centre, Department of Obstetrics and Gynaecology, First Faculty of Medicine, Charles University, General University Hospital in Prague, Prague, Czech Republic
| | - Henrica M J Werner
- Department of Obstetrics and Gynaecology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Clinical Science, University of Bergen, 5020, Bergen, Norway
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Meng J, Liu S, Zhu L, Zhu L, Wang H, Xie L, Guan Y, He J, Yang X, Zhou Z. Texture Analysis as Imaging Biomarker for recurrence in advanced cervical cancer treated with CCRT. Sci Rep 2018; 8:11399. [PMID: 30061666 PMCID: PMC6065361 DOI: 10.1038/s41598-018-29838-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 07/18/2018] [Indexed: 01/13/2023] Open
Abstract
This prospective study explored the application of texture features extracted from T2WI and apparent diffusion coefficient (ADC) maps in predicting recurrence of advanced cervical cancer patients treated with concurrent chemoradiotherapy (CCRT). We included 34 patients with advanced cervical cancer who underwent pelvic MR imaging before, during and after CCRT. Radiomic feature extraction was performed by using software at T2WI and ADC maps. The performance of texture parameters in predicting recurrence was evaluated. After a median follow-up of 31 months, eleven patients (32.4%) had recurrence. At four weeks after CCRT initiated, the most textural parameters (four T2 textural parameters and two ADC textural parameters) showed significant difference between the recurrence and nonrecurrence group (P values range, 0.002~0.046). Among them, RunLengthNonuniformity (RLN) from T2 and energy from ADC maps were the best selected predictors and together yield an AUC of 0.885. The support vector machine (SVM) classifier using ADC textural parameters performed best in predicting recurrence, while combining T2 textural parameters may add little value in prognosis. T2 and ADC textural parameters have potential as non-invasive imaging biomarkers in early predicting recurrence in advanced cervical cancer treated with CCRT.
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Affiliation(s)
- Jie Meng
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 210008, Nanjing, China
| | - Shunli Liu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 210008, Nanjing, China
| | - Lijing Zhu
- The Comprehensive Cancer Centre of Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, China, Nanjing, 210008
| | - Li Zhu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 210008, Nanjing, China
| | - Huanhuan Wang
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 210008, Nanjing, China
| | - Li Xie
- The Comprehensive Cancer Centre of Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, China, Nanjing, 210008
| | - Yue Guan
- School of Electronic Science and Engineering, Nanjing University, 210046, Nanjing, China
| | - Jian He
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 210008, Nanjing, China.
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, 30322, USA.
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 210008, Nanjing, China.
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