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Russo L, Bottazzi S, Kocak B, Zormpas-Petridis K, Gui B, Stanzione A, Imbriaco M, Sala E, Cuocolo R, Ponsiglione A. Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools. Eur Radiol 2024:10.1007/s00330-024-10947-6. [PMID: 39014086 DOI: 10.1007/s00330-024-10947-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/15/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024]
Abstract
OBJECTIVE To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). METHODS We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. RESULTS Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9-14) and METRICS score of 67.6% (IQR, 58.8-76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. CONCLUSIONS Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. CLINICAL RELEVANCE STATEMENT Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. KEY POINTS The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.
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Affiliation(s)
- Luca Russo
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Bottazzi
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey
| | - Konstantinos Zormpas-Petridis
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Benedetta Gui
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Evis Sala
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
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Huang ML, Ren J, Jin ZY, Liu XY, Li Y, He YL, Xue HD. Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:439-456. [PMID: 38349417 DOI: 10.1007/s11547-024-01765-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/03/2024] [Indexed: 03/16/2024]
Abstract
PURPOSE We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC). METHODS Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed. RESULTS Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively. CONCLUSION The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.
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Affiliation(s)
- Meng-Lin Huang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Obstetric and Gynecologic Diseases, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
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Jia Y, Hou L, Zhao J, Ren J, Li D, Li H, Cui Y. Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study. Front Oncol 2024; 14:1333020. [PMID: 38347846 PMCID: PMC10860747 DOI: 10.3389/fonc.2024.1333020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Objective To develop and validate a multiparametric MRI-based radiomics model for prediction of microsatellite instability (MSI) status in patients with endometrial cancer (EC). Methods A total of 225 patients from Center I including 158 in the training cohort and 67 in the internal testing cohort, and 132 patients from Center II were included as an external validation cohort. All the patients were pathologically confirmed EC who underwent pelvic MRI before treatment. The MSI status was confirmed by immunohistochemistry (IHC) staining. A total of 4245 features were extracted from T2-weighted imaging (T2WI), contrast enhanced T1-weighted imaging (CE-T1WI) and apparent diffusion coefficient (ADC) maps for each patient. Four feature selection steps were used, and then five machine learning models, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF), were built for MSI status prediction in the training cohort. Receiver operating characteristics (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of these models. Results The SVM model showed the best performance with an AUC of 0.905 (95%CI, 0.848-0.961) in the training cohort, and was subsequently validated in the internal testing cohort and external validation cohort, with the corresponding AUCs of 0.875 (95%CI, 0.762-0.988) and 0.862 (95%CI, 0.781-0.942), respectively. The DCA curve demonstrated favorable clinical utility. Conclusion We developed and validated a multiparametric MRI-based radiomics model with gratifying performance in predicting MSI status, and could potentially be used to facilitate the decision-making on clinical treatment options in patients with EC.
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Affiliation(s)
- Yaju Jia
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
- Department of Radiology, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China
| | - Lina Hou
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Jintao Zhao
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, China
| | - Dandan Li
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
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Guo W, Wang T, Lv B, Jiang J, Liu Y, Zhao P. Advances in Radiomics Research for Endometrial Cancer: A Comprehensive Review. J Cancer 2023; 14:3523-3531. [PMID: 38021155 PMCID: PMC10647186 DOI: 10.7150/jca.89347] [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: 08/21/2023] [Accepted: 10/08/2023] [Indexed: 12/01/2023] Open
Abstract
Endometrial cancer (EC) is a common gynecologic malignancy, with a rising trend in related mortality rates. The assessment based on imaging examinations contributes to the preoperative staging and surgical management of EC. However, conventional imaging diagnosis has limitations such as low accuracy and subjectivity. Radiomics, utilizing advanced feature analysis from medical images, extracts more information, ultimately establishing associations between imaging features and disease phenotypes. In recent years, radiomic studies on EC have emerged, employing radiomic features combined with clinical characteristics to model and predict histopathological features, protein expression, and clinical prognosis. This article elaborates on the application of radiomics in EC research and discusses its implications.
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Affiliation(s)
- Wenxiu Guo
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, China
| | - Tong Wang
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Binglin Lv
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Jie Jiang
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Yao Liu
- Department of Gynecology and Obstetrics, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Peng Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, China
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Lin Z, Wang T, Li Q, Bi Q, Wang Y, Luo Y, Feng F, Xiao M, Gu Y, Qiang J, Li H. Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study. Eur Radiol 2023; 33:5814-5824. [PMID: 37171486 DOI: 10.1007/s00330-023-09685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/17/2023] [Accepted: 02/26/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC). METHODS A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence. RESULTS The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence. CONCLUSIONS The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients. CLINICAL RELEVANCE STATEMENT An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients. KEY POINTS • The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.
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Affiliation(s)
- Zijing Lin
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Ting Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Qiong Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center/Cancer Hospital, 651 Dongfeng East Road, Guangzhou, 510060, China
| | - Qiu Bi
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, China
| | - Yaoxin Wang
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, China
| | - Yingwei Luo
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center/Cancer Hospital, 651 Dongfeng East Road, Guangzhou, 510060, China
| | - Feng Feng
- Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong, 226361, China
| | - Meiling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
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Li X, Marcus D, Russell J, Aboagye EO, Ellis LB, Sheeka A, Park WE, Bharwani N, Ghaem‐Maghami S, Rockall AG. An Integrated Clinical-MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer. J Magn Reson Imaging 2023; 57:1922-1933. [PMID: 36484309 PMCID: PMC10947322 DOI: 10.1002/jmri.28544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning. PURPOSE To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects. STUDY TYPE Retrospective. POPULATION Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years). FIELD STRENGTH/SEQUENCE 1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence. ASSESSMENT Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets. STATISTICAL TESTS A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model. RESULTS Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively. DATA CONCLUSION The proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xingfeng Li
- Department of Surgery and CancerImperial CollegeLondonUK
| | - Diana Marcus
- Department of Surgery and CancerImperial CollegeLondonUK
- Chelsea and Westminster Hospital NHS Foundation TrustLondonUK
| | - James Russell
- Imaging DepartmentImperial College Healthcare NHS TrustLondonUK
| | | | - Laura Burney Ellis
- Department of Surgery and CancerImperial CollegeLondonUK
- Imaging DepartmentImperial College Healthcare NHS TrustLondonUK
| | | | | | - Nishat Bharwani
- Department of Surgery and CancerImperial CollegeLondonUK
- Imaging DepartmentImperial College Healthcare NHS TrustLondonUK
| | | | - Andrea G. Rockall
- Department of Surgery and CancerImperial CollegeLondonUK
- Imaging DepartmentImperial College Healthcare NHS TrustLondonUK
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Zheng T, Pan J, Du D, Liang X, Yi H, Du J, Wu S, Liu L, Shi G. Preoperative assessment of high-grade endometrial cancer using a radiomic signature and clinical indicators. Future Oncol 2023; 19:587-601. [PMID: 37097730 DOI: 10.2217/fon-2022-0631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
Aim: To develop and validate a radiomics-based combined model (ModelRC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, ModelRC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864-0.962), 0.882 (95% CI: 0.779-0.955) and 0.881 (95% CI: 0.815-0.939) for the training, internal validation and external validation sets, respectively. Conclusion: ModelRC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.
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Affiliation(s)
- Tao Zheng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Jiangyang Pan
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Dan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Xin Liang
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Huiling Yi
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Juan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Shuo Wu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Lanxiang Liu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
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Di Donato V, Kontopantelis E, Cuccu I, Sgamba L, Golia D'Augè T, Pernazza A, Della Rocca C, Manganaro L, Catalano C, Perniola G, Palaia I, Tomao F, Giannini A, Muzii L, Bogani G. Magnetic resonance imaging-radiomics in endometrial cancer: a systematic review and meta-analysis. Int J Gynecol Cancer 2023:ijgc-2023-004313. [PMID: 37094971 DOI: 10.1136/ijgc-2023-004313] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
OBJECTIVE Endometrial carcinoma is the most common gynecological tumor in developed countries. Clinicopathological factors and molecular subtypes are used to stratify the risk of recurrence and to tailor adjuvant treatment. The present study aimed to assess the role of radiomics analysis in pre-operatively predicting molecular or clinicopathological prognostic factors in patients with endometrial carcinoma. METHODS Literature was searched for publications reporting radiomics analysis in assessing diagnostic performance of MRI for different outcomes. Diagnostic accuracy performance of risk prediction models was pooled using the metandi command in Stata. RESULTS A search of MEDLINE (PubMed) resulted in 153 relevant articles. Fifteen articles met the inclusion criteria, for a total of 3608 patients. MRI showed pooled sensitivity and specificity 0.785 and 0.814, respectively, in predicting high-grade endometrial carcinoma, deep myometrial invasion (pooled sensitivity and specificity 0.743 and 0.816, respectively), lymphovascular space invasion (pooled sensitivity and specificity 0.656 and 0.753, respectively), and nodal metastasis (pooled sensitivity and specificity 0.831 and 0.736, respectively). CONCLUSIONS Pre-operative MRI-radiomics analyses in patients with endometrial carcinoma is a good predictor of tumor grading, deep myometrial invasion, lymphovascular space invasion, and nodal metastasis.
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Affiliation(s)
- Violante Di Donato
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Evangelos Kontopantelis
- Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK
| | - Ilaria Cuccu
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Ludovica Sgamba
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Tullio Golia D'Augè
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Angelina Pernazza
- Department of Medical-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Rome, Italy
| | - Carlo Della Rocca
- Department of Medical-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Rome, Italy
| | - Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Giorgia Perniola
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Innocenza Palaia
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Federica Tomao
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Andrea Giannini
- Department of Medical and Surgical Sciences and Translational Medicine, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Ludovico Muzii
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Giorgio Bogani
- Department of Gynecologic Oncology, IRCCS National Cancer Institute, Milan, Italy
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Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study. J Pers Med 2022; 12:jpm12111854. [PMID: 36579601 PMCID: PMC9696574 DOI: 10.3390/jpm12111854] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/11/2022] [Accepted: 10/31/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE To develop and validate magnetic resonance (MR) imaging-based radiomics models for high-risk endometrial cancer (EC) prediction preoperatively, to be able to estimate deep myometrial invasion (DMI) and lymphovascular space invasion (LVSI), and to discriminate between low-risk and other categories of risk as proposed by ESGO/ESTRO/ESP (European Society of Gynaecological Oncology-European Society for Radiotherapy & Oncology and European Society of Pathology) guidelines. METHODS This retrospective study included 96 women with EC who underwent 1.5-T MR imaging before surgical staging between April 2009 and May 2019 in two referral centers divided into training (T = 73) and validation cohorts (V = 23). Radiomics features were extracted using the MODDICOM library with manual delineation of whole-tumor volume on MR images (axial T2-weighted). Diagnostic performances of radiomic models were evaluated by area under the receiver operating characteristic (ROC) curve in training (AUCT) and validation (AUCV) cohorts by using a subset of the most relevant texture features tested individually in univariate analysis using Wilcoxon-Mann-Whitney. RESULTS A total of 228 radiomics features were extracted and ultimately limited to 38 for DMI, 29 for LVSI, and 15 for risk-classes prediction for logistic radiomic modeling. Whole-tumor radiomic models yielded an AUCT/AUCV of 0.85/0.68 in DMI estimation, 0.92/0.81 in LVSI prediction, and 0.84/0.76 for differentiating low-risk vs other risk classes (intermediate/high-intermediate/high). CONCLUSION MRI-based radiomics has great potential in developing advanced prognostication in EC.
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Bonde A, Andreazza Dal Lago E, Foster B, Javadi S, Palmquist S, Bhosale P. Utility of the Diffusion Weighted Sequence in Gynecological Imaging: Review Article. Cancers (Basel) 2022; 14:cancers14184468. [PMID: 36139628 PMCID: PMC9496793 DOI: 10.3390/cancers14184468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/30/2022] [Accepted: 09/08/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Diffusion weighted imaging (DWI) is a magnetic resonance imaging sequence with diverse clinical applications in malignant and nonmalignant gynecological conditions. It provides vital supplemental information in the diagnosis and management of various gynecological conditions. Radiologists should be aware of fundamental concepts, clinical applications and pitfalls of DWI. Additionally we briefly discuss potential scope of newer advanced techniques based on DWI including diffusion tensor imaging and diffusion-weighted whole-body imaging with background signal suppression. Abstract Functional imaging with diffusion-weighted imaging (DWI) is a complementary tool to conventional diagnostic magnetic resonance imaging sequences. It is being increasingly investigated to predict tumor response and assess tumor recurrence. We elucidate the specific technical modifications of DWI preferred for gynecological imaging, including the different b-values and planes for image acquisition. Additionally, we discuss the problems and potential pitfalls encountered during DWI interpretation and ways to overcome them. DWI has a wide range of clinical applications in malignant and non-malignant gynecological conditions. It provides supplemental information helpful in diagnosing and managing tubo-ovarian abscess, uterine fibroids, endometriosis, adnexal torsion, and dermoid. Similarly, DWI has diverse applications in gynecological oncology in diagnosis, staging, detection of recurrent disease, and tumor response assessment. Quantitative evaluation with apparent diffusion coefficient (ADC) measurement is being increasingly evaluated for correlation with various tumor parameters in managing gynecological malignancies aiding in preoperative treatment planning. Newer advanced DWI techniques of diffusion tensor imaging (DTI) and whole body DWI with background suppression (DWIBS) and their potential uses in pelvic nerve mapping, preoperative planning, and fertility-preserving surgeries are briefly discussed.
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Affiliation(s)
- Apurva Bonde
- Department of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Correspondence:
| | | | - Bryan Foster
- Department of Radiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sanaz Javadi
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sarah Palmquist
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Priya Bhosale
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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