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Wei ZY, Zhang Z, Zhao DL, Zhao WM, Meng YG. Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer. World J Clin Cases 2024; 12:5908-5921. [DOI: 10.12998/wjcc.v12.i26.5908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/19/2024] [Accepted: 07/03/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC.
AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.
METHODS The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.
RESULTS Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature.
CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.
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Affiliation(s)
- Zhi-Yao Wei
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Zhe Zhang
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Dong-Li Zhao
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Wen-Ming Zhao
- National Genomics Data Center and Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100700, China
| | - Yuan-Guang Meng
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
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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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Cao Y, Zhang W, Wang X, Lv X, Zhang Y, Guo K, Ren S, Li Y, Wang Z, Chen J. Multiparameter MRI-based radiomics analysis for preoperative prediction of type II endometrial cancer. Heliyon 2024; 10:e32940. [PMID: 38988546 PMCID: PMC11234004 DOI: 10.1016/j.heliyon.2024.e32940] [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: 10/09/2023] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/12/2024] Open
Abstract
Objectives This study aimed to develop and validate a radiomics nomogram based on multiparameter MRI for preoperative differentiation of type II and type I endometrial carcinoma (EC). Methods A total of 403 EC patients from two centers were retrospectively recruited (training cohort, 70 %; validation cohort, 30 %). Radiomics features were extracted from T2-weighted imaging, dynamic contrast-enhanced T1-weighted imaging at delayed phase(DCE4), and apparent diffusion coefficient (ADC) maps. Following dimensionality reduction, radiomics models were developed by logistic regression (LR), random forest (RF), bootstrap aggregating (Bagging), support vector machine (SVM), artificial neural network (ANN), and naive bayes (NB) algorithms. The diagnostic performance of each radiomics model was evaluated using the ROC curve. A nomogram was constructed by incorporating the optimal radiomics signatures with significant clinical-radiological features and immunohistochemistry (IHC) markers obtained from preoperative curettage specimens. The diagnostic performance and clinical value of the nomogram were evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Results Among the radiomics models, the NB model, developed from 12 radiomics features derived from ADC and DCE4 sequences, exhibited strong performance in both training and validation sets, with the AUC values of 0.927 and 0.869, respectively. The nomogram, incorporating the radiomics model with significant clinical-radiological features and IHC markers, demonstrated superior performance in both the training (AUC = 0.951) and the validation sets (AUC = 0.915). Additionally, it exhibited excellent calibration and clinical utility. Conclusions The radiomics nomogram has great potential to differentiate type II from type I EC, which may be an effective tool to guide clinical decision-making for EC patients.
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Affiliation(s)
- Yingying Cao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Wei Zhang
- Department of Radiology, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Xiaorong Wang
- Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
- Taixing People's Hospital, Jiangsu, China
| | - Xiaojing Lv
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Yaping Zhang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Kai Guo
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Shuai Ren
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Yuan Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Jingya Chen
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
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Halle MK, Hodneland E, Wagner-Larsen KS, Lura NG, Fasmer KE, Berg HF, Stokowy T, Srivastava A, Forsse D, Hoivik EA, Woie K, Bertelsen BI, Krakstad C, Haldorsen IS. Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer. Sci Rep 2024; 14:11339. [PMID: 38760387 PMCID: PMC11101482 DOI: 10.1038/s41598-024-61271-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/03/2024] [Indexed: 05/19/2024] Open
Abstract
Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage disease, and few effective treatments exist. Preoperative diagnostic imaging is common in high-income countries and MRI measured tumor size routinely guides treatment allocation of cervical cancer patients. Recently, the role of MRI radiomics has been recognized. However, its potential to independently predict survival and treatment response requires further clarification. This retrospective cohort study demonstrates how non-invasive, preoperative, MRI radiomic profiling may improve prognostication and tailoring of treatments and follow-ups for cervical cancer patients. By unsupervised clustering based on 293 radiomic features from 132 patients, we identify three distinct clusters comprising patients with significantly different risk profiles, also when adjusting for FIGO stage and age. By linking their radiomic profiles to genomic alterations, we identify putative treatment targets for the different patient clusters (e.g., immunotherapy, CDK4/6 and YAP-TEAD inhibitors and p53 pathway targeting treatments).
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Affiliation(s)
- Mari Kyllesø Halle
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Erlend Hodneland
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Mathematics, University of Bergen, Bergen, Norway
| | - Kari S Wagner-Larsen
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Njål G Lura
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kristine E Fasmer
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Hege F Berg
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Tomasz Stokowy
- Genomics Core Facility, Department of Clinical Science, University of Bergen, Bergen, Norway
- Section of Bioinformatics, Clinical Laboratory, Haukeland University Hospital, Bergen, Norway
| | - Aashish Srivastava
- Genomics Core Facility, Department of Clinical Science, University of Bergen, Bergen, Norway
- Section of Bioinformatics, Clinical Laboratory, Haukeland University Hospital, Bergen, Norway
| | - David Forsse
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Erling A Hoivik
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Kathrine Woie
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Bjørn I Bertelsen
- Department of Pathology, Haukeland University Hospital, Bergen, Norway
| | - Camilla Krakstad
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
| | - Ingfrid S Haldorsen
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
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Ning Y, Liu W, Wang H, Zhang F, Chen X, Wang Y, Wang T, Yang G, Zhang H. Determination of p53abn endometrial cancer: a multitask analysis using radiological-clinical nomogram on MRI. Br J Radiol 2024; 97:954-963. [PMID: 38538868 PMCID: PMC11075989 DOI: 10.1093/bjr/tqae066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 01/11/2024] [Accepted: 03/21/2024] [Indexed: 05/09/2024] Open
Abstract
OBJECTIVES We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MRI. METHODS We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the nonendometrioid, high-risk, and P53abn EC group. RESULTS The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). CONCLUSION In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC. ADVANCES IN KNOWLEDGE (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845).
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Affiliation(s)
- Yan Ning
- Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
| | - Wei Liu
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
| | - Haijie Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Feiran Zhang
- Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
| | - Xiaojun Chen
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Tianping Wang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China
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Santoro M, Zybin V, Coada CA, Mantovani G, Paolani G, Di Stanislao M, Modolon C, Di Costanzo S, Lebovici A, Ravegnini G, De Leo A, Tesei M, Pasquini P, Lovato L, Morganti AG, Pantaleo MA, De Iaco P, Strigari L, Perrone AM. Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study. Cancers (Basel) 2024; 16:1570. [PMID: 38672651 PMCID: PMC11048510 DOI: 10.3390/cancers16081570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/07/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. METHODS Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. RESULTS A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85). CONCLUSIONS CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.
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Affiliation(s)
- Miriam Santoro
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.S.); (G.P.); (L.S.)
| | - Vladislav Zybin
- Pediatric and Adult CardioThoracic and Vascular, Oncohematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (V.Z.); (C.M.); (L.L.)
| | | | - Giulia Mantovani
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
| | - Giulia Paolani
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.S.); (G.P.); (L.S.)
| | - Marco Di Stanislao
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
| | - Cecilia Modolon
- Pediatric and Adult CardioThoracic and Vascular, Oncohematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (V.Z.); (C.M.); (L.L.)
| | - Stella Di Costanzo
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
| | - Andrei Lebovici
- Radiology and Imaging Department, County Emergency Hospital, 400347 Cluj-Napoca, Romania;
- Surgical Specialties Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Gloria Ravegnini
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy;
| | - Antonio De Leo
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
- Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Marco Tesei
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
| | - Pietro Pasquini
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
| | - Luigi Lovato
- Pediatric and Adult CardioThoracic and Vascular, Oncohematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (V.Z.); (C.M.); (L.L.)
| | - Alessio Giuseppe Morganti
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Maria Abbondanza Pantaleo
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
- Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Pierandrea De Iaco
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
| | - Lidia Strigari
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (M.S.); (G.P.); (L.S.)
| | - Anna Myriam Perrone
- Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (G.M.); (M.D.S.); (S.D.C.); (M.T.); (P.P.)
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy; (A.D.L.); (A.G.M.); (M.A.P.)
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7
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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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8
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Guan W, Wang Y, Zhao H, Lu H, Zhang S, Liu J, Shi B. Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability. Front Neurosci 2024; 18:1275487. [PMID: 38410157 PMCID: PMC10894972 DOI: 10.3389/fnins.2024.1275487] [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: 08/10/2023] [Accepted: 01/15/2024] [Indexed: 02/28/2024] Open
Abstract
Background The occurrence of lymph node metastasis (LNM) is one of the critical factors in determining the staging, treatment and prognosis of cervical cancer (CC). Heart rate variability (HRV) is associated with LNM in patients with CC. The purpose of this study was to validate the feasibility of machine learning (ML) models constructed with preoperative HRV as a feature of CC patients in predicting CC LNM. Methods A total of 292 patients with pathologically confirmed CC admitted to the Department of Gynecological Oncology of the First Affiliated Hospital of Bengbu Medical University from November 2020 to September 2023 were included in the study. The patient' preoperative 5-min electrocardiogram data were collected, and HRV time-domain, frequency-domain and non-linear analyses were subsequently performed, and six ML models were constructed based on 32 parameters. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results Among the 6 ML models, the random forest (RF) model showed the best predictive performance, as specified by the following metrics on the test set: AUC (0.852), accuracy (0.744), sensitivity (0.783), and specificity (0.785). Conclusion The RF model built with preoperative HRV parameters showed superior performance in CC LNM prediction, but multicenter studies with larger datasets are needed to validate our findings, and the physiopathological mechanisms between HRV and CC LNM need to be further explored.
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Affiliation(s)
- Weizheng Guan
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Yuling Wang
- Department of Gynecologic Oncology, The First Affiliated Hospital, Bengbu Medical University, Bengbu, Anhui, China
| | - Huan Zhao
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Hui Lu
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Sai Zhang
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Jian Liu
- Department of Gynecologic Oncology, The First Affiliated Hospital, Bengbu Medical University, Bengbu, Anhui, China
| | - Bo Shi
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
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9
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Yang S, Zhang W, Liu C, Li C, Hua K. Predictive value and potential association of PET/CT radiomics on lymph node metastasis of cervical cancer. Ann Med Surg (Lond) 2024; 86:805-810. [PMID: 38333288 PMCID: PMC10849352 DOI: 10.1097/ms9.0000000000001412] [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: 09/18/2023] [Accepted: 10/09/2023] [Indexed: 02/10/2024] Open
Abstract
Objective Due to the information-rich nature of positron emission tomography/computed tomography (PET/CT) images, the authors hope to explore radiomics features that could distinguish metastatic lymph nodes (LNs) from hypermetabolic benign LNs, in addition to conventional indicators. Methods PET/CT images of 106 patients with early-stage cervical cancer from 2019 to 2021 were retrospectively analyzed. The tumor lesions and LN regions of PET/CT images were outlined with SeeIt, and then radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select features. The final selected radiomics features of LNs were used as predictors to construct a machine learning model to predict LN metastasis. Results The authors determined two morphological coefficient characteristics of cervical lesions (shape - major axis length and shape - mesh volume), one first order characteristics of LNs (first order - 10 percentile) and two gray-level co-occurrence matrix (GLCM) characteristics of LNs (GLCM - id and GLCM - inverse variance) were closely related to LN metastasis. Finally, a neural network was constructed based on the radiomic features of the LNs. The area under the curve of receiver operating characteristic (AUC-ROC) of the model was 0.983 in the training set and 0.860 in the test set. Conclusion The authors constructed and demonstrated a neural network based on radiomics features of PET/CT to evaluate the risk of single LN metastasis in early-stage cervical cancer.
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Affiliation(s)
- Shimin Yang
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University
| | - Wenrui Zhang
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People’s Republic of China
| | - Chunli Liu
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People’s Republic of China
| | - Chunbo Li
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University
| | - Keqin Hua
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University
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10
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Leo E, Stanzione A, Miele M, Cuocolo R, Sica G, Scaglione M, Camera L, Maurea S, Mainenti PP. Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows. J Clin Med 2023; 13:226. [PMID: 38202233 PMCID: PMC10779496 DOI: 10.3390/jcm13010226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 12/23/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.
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Affiliation(s)
- Elisabetta Leo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Mariaelena Miele
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Luigi Camera
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Council of Research (CNR), 80131 Naples, Italy
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11
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Kido A, Himoto Y, Kurata Y, Minamiguchi S, Nakamoto Y. Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023. J Magn Reson Imaging 2023. [PMID: 38146775 DOI: 10.1002/jmri.29161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 12/27/2023] Open
Abstract
The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Aki Kido
- Department Radiology, Toyama University Hospital, Toyama, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yasuhisa Kurata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | | | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
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12
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Huang W, Jin Y, Jiang L, Liang M. Radiomics optimizing the evaluation of endometrial receptivity for women with unexplained recurrent pregnancy loss. Front Endocrinol (Lausanne) 2023; 14:1181058. [PMID: 37795355 PMCID: PMC10545880 DOI: 10.3389/fendo.2023.1181058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/24/2023] [Indexed: 10/06/2023] Open
Abstract
Background The optimization of endometrial receptivity (ER) through individualized therapies has been shown to enhance the likelihood of successful gestation. However, current practice lacks comprehensive methods for evaluating the ER of patients with recurrent pregnancy loss (RPL). Radiomics, an emerging AI-based technique that enables the extraction of mineable information from medical images, holds potential to offer a more objective and quantitative approach to ER assessment. This innovative tool may facilitate a deeper understanding of the endometrial environment and enable clinicians to optimize ER evaluation in RPL patients. Objective This study aimed to identify ultrasound radiomics features associated with ER, with the purpose of predicting successful ongoing pregnancies in RPL patients, and to assess the predictive accuracy of these features against regular ER parameters. Methods This retrospective, controlled study involved 262 patients with unexplained RPL and 273 controls with a history of uncomplicated full-term pregnancies. Radiomics features were extracted from ultrasound endometrial segmentation images to derive a radiomics score (rad-score) for each participant. Associations between rad-scores, baseline clinical variables, and sonographic data were evaluated using univariate and multivariate logistic regression analyses to identify potential indicators of RPL. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive accuracy of the rad-score and other identified indicators in discriminating RPL cases. Furthermore, the relationships between age and these identified indicators were assessed via Pearson correlation analysis. Results From the 1312 extracted radiomics features, five non-zero coefficient radiomics signatures were identified as significantly associated with RPL, forming the basis of the rad-score. Following multivariate logistic regression analysis, age, spiral artery pulsatility index (SA-PI), vascularisation index (VI), and rad-score emerged as independent correlates of RPL (all P<0.05). ROC curve analyses revealed the superior discriminative capability of the rad-score (AUC=0.882) over age (AUC=0.778), SA-PI (AUC=0.771), and VI (AUC=0.595). There were notable correlations between age and rad-score (r=0.275), VI (r=-0.224), and SA-PI (r=0.211), indicating age-related variations in RPL predictors. Conclusion This study revealed a significant association between unexplained RPL and elevated endometrial rad-scores during the WOI. Furthermore, it demonstrated the potential of rad-scores as a promising predictive tool for successful ongoing pregnancies in RPL patients.
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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, Dessi M, Marcus D, Russell J, Aboagye EO, Ellis LB, Sheeka A, Park WHE, Bharwani N, Ghaem-Maghami S, Rockall AG. Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features. Cancers (Basel) 2023; 15:cancers15082209. [PMID: 37190137 DOI: 10.3390/cancers15082209] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
PURPOSE To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images. METHODS A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models. RESULTS Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively. CONCLUSION It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.
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Affiliation(s)
- Xingfeng Li
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London W12 0NN, UK
| | - Michele Dessi
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London W12 0NN, UK
| | - Diana Marcus
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London W12 0NN, UK
- Chelsea and Westminster Hospital, 369 Fulham Rd., London SW10 9NH, UK
| | - James Russell
- The Imaging Department, Imperial College Healthcare NHS Trust, UK Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London W12 0NN, UK
| | - Laura Burney Ellis
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London W12 0NN, UK
| | - Alexander Sheeka
- The Imaging Department, Imperial College Healthcare NHS Trust, UK Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Won-Ho Edward Park
- The Imaging Department, Imperial College Healthcare NHS Trust, UK Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Nishat Bharwani
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London W12 0NN, UK
- The Imaging Department, Imperial College Healthcare NHS Trust, UK Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Sadaf Ghaem-Maghami
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London W12 0NN, UK
| | - Andrea G Rockall
- Department of Surgery and Cancer, Imperial College Hammersmith Campus, Du Cane Road, London W12 0NN, UK
- The Imaging Department, Imperial College Healthcare NHS Trust, UK Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
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15
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Qian WL, Chen Q, Zhang JB, Xu JM, Hu CH. RESOLVE-based radiomics in cervical cancer: improved image quality means better feature reproducibility? Clin Radiol 2023; 78:e469-e476. [PMID: 37029000 DOI: 10.1016/j.crad.2023.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 04/07/2023]
Abstract
AIM To compare the reproducibility of apparent diffusion coefficient (ADC)-based radiomic features between readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) and single-shot echo-planar diffusion-weighted imaging (SS-EPI DWI) in cervical cancer. MATERIALS AND METHODS The RESOLVE and SS-EPI DWI images of 36 patients with histopathologically confirmed cervical cancer were collected retrospectively. Two observers independently delineated the whole tumour on RESOLVE and SS-EPI DWI, and then copied them to the corresponding ADC maps. Shape, first-order, and texture features were extracted from ADC maps in the original and filtered (Laplacian of Gaussian [LoG] and wavelet) images. Thereafter, 1,316 features were generated in each RESOLVE and SS-EPI DWI, respectively. The reproducibility of radiomic features was assessed using intraclass correlation coefficient (ICC). RESULTS In the original images, RESOLVE showed 92.86%, 66.67%, and 86.67% of features with excellent reproducibility in shape, first-order, and texture features, while SS-EPI DWI showed 85.71%, 72.22%, and 60% of features with excellent reproducibility, respectively. In the LoG and wavelet filtered images, RESOLVE had 56.77% and 65.32% of features with excellent reproducibility and SS-EPI DWI had 44.95% and 61.96% of features with excellent reproducibility, respectively. CONCLUSION Compared with SS-EPI DWI, the feature reproducibility of RESOLVE was better in cervical cancer, especially for texture features. The filtered images cannot improve the feature reproducibility compared with the original images for both SS-EPI DWI and RESOLVE.
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16
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Lin Z, Wang T, Li H, Xiao M, Ma X, Gu Y, Qiang J. Magnetic resonance-based radiomics nomogram for predicting microsatellite instability status in endometrial cancer. Quant Imaging Med Surg 2023; 13:108-120. [PMID: 36620141 PMCID: PMC9816750 DOI: 10.21037/qims-22-255] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 09/05/2022] [Indexed: 11/30/2022]
Abstract
Background Microsatellite instability (MSI) status is an important indicator for screening patients with endometrial cancer (EC) who have potential Lynch syndrome (LS) and may benefit from immunotherapy. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics nomogram for the prediction of MSI status in EC. Methods A total of 296 patients with histopathologically diagnosed EC were enrolled, and their MSI status was determined using immunohistochemical (IHC) analysis. Patients were randomly divided into the training cohort (n=236) and the validation cohort (n=60) at a ratio of 8:2. To predict the MSI status in EC, the tumor radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images, which in turn were selected using one-way analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm to build the radiomics signature (radiomics score; radscore) model. Five clinicopathologic characteristics were used to construct a clinicopathologic model. Finally, the nomogram model combining radscore and clinicopathologic characteristics was constructed. The performance of the three models was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA). Results Totals of 21 radiomics features and five clinicopathologic characteristics were selected to develop the radscore and clinicopathological models. The radscore and clinicopathologic models achieved an area under the curve (AUC) of 0.752 and 0.600, respectively, in the training cohort; and of 0.723 and 0.615, respectively, in the validation cohort. The radiomics nomogram model showed improved discrimination efficiency compared with the radscore and clinicopathologic models, with an AUC of 0.773 and 0.740 in the training and validation cohorts, respectively. The calibration curve analysis and DCA showed favorable calibration and clinical utility of the nomogram model. Conclusions The nomogram incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for the prediction of MSI status in EC.
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Affiliation(s)
- Zijing Lin
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China;,Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ting Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Meiling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Xiaoliang Ma
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China;,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
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17
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MRI- and Histologic-Molecular-Based Radio-Genomics Nomogram for Preoperative Assessment of Risk Classes in Endometrial Cancer. Cancers (Basel) 2022; 14:cancers14235881. [PMID: 36497362 PMCID: PMC9739755 DOI: 10.3390/cancers14235881] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/17/2022] [Accepted: 11/24/2022] [Indexed: 12/02/2022] Open
Abstract
High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performed. We aimed to develop MRI-based radio-genomic models able to preoperatively assess lymph-vascular space invasion (LVSI) and discriminate between low- and high-risk EC according to the ESMO-ESGO-ESTRO 2020 guidelines, which include molecular risk classification proposed by "ProMisE". This is a retrospective, multicentric study that included 64 women with EC who underwent 3T-MRI before a hysterectomy. Radiomics features were extracted from T2WI images and apparent diffusion coefficient maps (ADC) after manual segmentation of the gross tumor volume. We constructed a multiple logistic regression approach from the most relevant radiomic features to distinguish between low- and high-risk classes under the ESMO-ESGO-ESTRO 2020 guidelines. A similar approach was taken to assess LVSI. Model diagnostic performance was assessed via ROC curves, accuracy, sensitivity and specificity on training and test sets. The LVSI predictive model used a single feature from ADC as a predictor; the risk class model used two features as predictors from both ADC and T2WI. The low-risk predictive model showed an AUC of 0.74 with an accuracy, sensitivity, and specificity of 0.74, 0.76, 0.94; the LVSI model showed an AUC of 0.59 with an accuracy, sensitivity, and specificity of 0.60, 0.50, 0.61. MRI-based radio-genomic models are useful for preoperative EC risk stratification and may facilitate therapeutic management.
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Daoud T, Sardana S, Stanietzky N, Klekers AR, Bhosale P, Morani AC. Recent Imaging Updates and Advances in Gynecologic Malignancies. Cancers (Basel) 2022; 14:cancers14225528. [PMID: 36428624 PMCID: PMC9688526 DOI: 10.3390/cancers14225528] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/31/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022] Open
Abstract
Gynecologic malignancies are among the most common cancers in women worldwide and account for significant morbidity and mortality. Management and consequently overall patient survival is reliant upon early detection, accurate staging and early detection of any recurrence. Ultrasound, Computed Tomography (CT), Magnetic resonance imaging (MRI) and Positron Emission Tomography-Computed Tomography (PET-CT) play an essential role in the detection, characterization, staging and restaging of the most common gynecologic malignancies, namely the cervical, endometrial and ovarian malignancies. Recent advances in imaging including functional MRI, hybrid imaging with Positron Emission Tomography (PET/MRI) contribute even more to lesion specification and overall role of imaging in gynecologic malignancies. Radiomics is a neoteric approach which aspires to enhance decision support by extracting quantitative information from radiological imaging.
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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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Jiang X, Song J, Duan S, Cheng W, Chen T, Liu X. MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer. Br J Radiol 2022; 95:20211229. [PMID: 35604668 PMCID: PMC10162065 DOI: 10.1259/bjr.20211229] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 03/21/2022] [Accepted: 05/06/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer. METHODS A total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort (n = 129) and internal validation cohort (n = 54). The radiomic features were extracted from the pre-operative T1 contrast-enhanced (T1CE) and T2 weighted image of each patient. Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazard model were used for feature selection, and the rad-score (RS) of each patient were evaluated individually. The clinicopathology model, T1CE_RS model, T1CE + T2_RS model, and clinicopathology combined with T1CE_RS model were established and compared. Patients were divided into high- and low-risk groups according to the optimum cut-off values of four models. RESULTS T1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773). CONCLUSION The combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up. ADVANCES IN KNOWLEDGE A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.
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Affiliation(s)
- Xiaoting Jiang
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiacheng Song
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Wenjun Cheng
- Department of Gynaecology and Obstetrics, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ting Chen
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xisheng Liu
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Radiomics in endometrial cancer and beyond - a perspective from the editors of the EJR. Eur J Radiol 2022; 150:110266. [PMID: 35338953 DOI: 10.1016/j.ejrad.2022.110266] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 03/16/2022] [Indexed: 01/29/2023]
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22
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Nougaret S, Vargas HA, Sala E. BJR female genitourinary oncology special feature: introductory editorial. Br J Radiol 2021; 94:20219003. [PMID: 34415200 DOI: 10.1259/bjr.20219003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Stephanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, University of Montpellier, Montpellier, France.,INSERM, Montpellier Cancer Research Institute, U1194, University of Montpellier, Montpellier, France
| | | | - Evis Sala
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, UK.,Department of Radiology and Cancer Research UK Cambridge Centre, University of Cambridge School of Clinical Medicine, Hills Road, Cambridge, UK
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