{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:56:22Z","timestamp":1790574982581,"version":"4.1.0"},"reference-count":43,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T00:00:00Z","timestamp":1790467200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T00:00:00Z","timestamp":1790467200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Medical Radiation Sci"],"abstract":"ABSTRACT<\/jats:title>\n \n Introduction<\/jats:title>\n Cervical cancer management relies heavily on magnetic resonance imaging (MRI) for staging and treatment planning; however, manual segmentation is time\u2010consuming and prone to variability. This systematic review aimed to evaluate the performance and clinical potential of machine learning (ML) and deep learning (DL) algorithms for automated cervical cancer segmentation on MRI.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A systematic review guided by the Preferred Reporting Items for Systematic Reviews and Meta\u2010Analyses (PRISMA) (PROSPERO: CRD420251247441) was conducted by searching PubMed\/MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, and Google Scholar until December 2025. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies\u20102 (QUADAS\u20102). MRI\u2010based segmentation studies were primarily included, while computed tomography (CT)\u2010 and positron emission tomography (PET)\u2010based multimodal studies were analysed as complementary evidence. Due to substantial heterogeneity, findings were synthesized narratively, and certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Thirty studies were included, mainly evaluating segmentation of tumours, high\u2010risk clinical target volumes, and organs at risk. DL models, particularly U\u2010Net variants, convolutional neural networks, and transformer\u2010based architectures, demonstrated promising performance, with reported Dice similarity coefficients ranging from 0.60 to 0.93. However, performance varied according to target structure, tumour characteristics, and imaging protocols. Overall risk of bias showed \u201csome concerns,\u201d mainly due to patient selection, reference standards, and limited external validation. GRADE assessment indicated low certainty of evidence due to heterogeneity, methodological limitations, and imprecision.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n ML and DL algorithms show promising potential for automated cervical cancer segmentation on MRI, with potential benefits for efficiency and reproducibility. However, further multicenter studies with standardized protocols and external validation are required before routine clinical implementation.<\/jats:p>\n <\/jats:sec>\n \n Trial Registration<\/jats:title>\n PRISMA guidelines and was prospectively registered in PROSPERO: CRD420251247441<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmrs.70127","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:21:19Z","timestamp":1790572879000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on\n MRI<\/scp>\n : A Systematic Review"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8654-4548","authenticated-orcid":false,"given":"Somayeh Haji","family":"Ahmadi","sequence":"first","affiliation":[{"name":"Department of Radiology, School of Medicine Isfahan University of Medical Sciences Isfahan Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sina Soltan","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine Isfahan University of Medical Sciences Isfahan Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5149-8914","authenticated-orcid":false,"given":"Abolfazl","family":"Koozari","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, School of Medicine Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1186-353X","authenticated-orcid":false,"given":"Mohammadreza","family":"Elhaie","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine Isfahan University of Medical Sciences Isfahan Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,27]]},"reference":[{"issue":"10167","key":"e_1_2_12_2_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0140-6736(18)32470-X","article-title":"Cervical Cancer","volume":"393","author":"Cohen P. A.","year":"2019","journal-title":"Lancet"},{"issue":"6","key":"e_1_2_12_3_1","doi-asserted-by":"crossref","first-page":"720","DOI":"10.21147\/j.issn.1000-9604.2020.06.05","article-title":"Cervical Cancer: Epidemiology, Risk Factors and Screening","volume":"32","author":"Zhang S.","year":"2020","journal-title":"Chinese Journal of Cancer Research"},{"issue":"1","key":"e_1_2_12_4_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/0090-8258(89)90604-5","article-title":"Magnetic Resonance Imaging in Cervical Cancer: A Basis for Objective Classification","volume":"33","author":"Burghardt E.","year":"1989","journal-title":"Gynecologic Oncology"},{"issue":"5","key":"e_1_2_12_5_1","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s13244-017-0567-0","article-title":"The Value of Advanced MRI Techniques in the Assessment of Cervical Cancer: A Review","volume":"8","author":"Dappa E.","year":"2017","journal-title":"Insights Into Imaging"},{"issue":"20","key":"e_1_2_12_6_1","doi-asserted-by":"crossref","DOI":"10.3390\/cancers15205105","article-title":"An Update on the Role of MRI in Treatment Stratification of Patients With Cervical Cancer","volume":"15","author":"Shakur A.","year":"2023","journal-title":"Cancers"},{"issue":"5","key":"e_1_2_12_7_1","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.3390\/electronics12051199","article-title":"Techniques and Challenges of Image Segmentation: A Review","volume":"12","author":"Yu Y.","year":"2023","journal-title":"Electronics"},{"issue":"5","key":"e_1_2_12_8_1","doi-asserted-by":"crossref","DOI":"10.1118\/1.4871620","article-title":"Vision 20\/20: Perspectives on Automated Image Segmentation for Radiotherapy","volume":"41","author":"Sharp G.","year":"2014","journal-title":"Medical Physics"},{"key":"e_1_2_12_9_1","doi-asserted-by":"crossref","first-page":"134557","DOI":"10.1109\/ACCESS.2022.3230983","article-title":"A Systematic Literature Review on Machine Learning and Deep Learning Methods for Semantic Segmentation","volume":"10","author":"Sohail A.","year":"2022","journal-title":"IEEE Access"},{"issue":"21","key":"e_1_2_12_10_1","first-page":"1974","article-title":"Machine Learning and Deep Learning for Brain Tumor MRI Image Segmentation","volume":"248","author":"Khan M. K. H.","year":"2023","journal-title":"Experimental Biology and Medicine"},{"issue":"9","key":"e_1_2_12_11_1","doi-asserted-by":"crossref","first-page":"27001","DOI":"10.1007\/s11042-023-16520-5","article-title":"Cancer Detection and Segmentation Using Machine Learning and Deep Learning Techniques: A Review","volume":"83","author":"Rai H. M.","year":"2024","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_2_12_12_1","first-page":"A5","volume-title":"PRISMA 2020","author":"Tugwell P.","year":"2021"},{"issue":"2","key":"e_1_2_12_13_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1080\/02763869.2019.1588072","article-title":"PROSPERO: An International Register of Systematic Review Protocols","volume":"38","author":"Schiavo J. H.","year":"2019","journal-title":"Medical Reference Services Quarterly"},{"issue":"8","key":"e_1_2_12_14_1","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS\u20102: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies","volume":"155","author":"Whiting P. F.","year":"2011","journal-title":"Annals of Internal Medicine"},{"issue":"21","key":"e_1_2_12_15_1","doi-asserted-by":"crossref","first-page":"3381","DOI":"10.3390\/diagnostics13213381","article-title":"Deep Learning Framework With Multi\u2010Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging","volume":"13","author":"Kalantar R.","year":"2023","journal-title":"Diagnostics"},{"issue":"3","key":"e_1_2_12_16_1","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1007\/s00330-019-06467-3","article-title":"Deep Learning for Fully Automated Tumor Segmentation and Extraction of Magnetic Resonance Radiomics Features in Cervical Cancer","volume":"30","author":"Lin Y.\u2010C.","year":"2020","journal-title":"European Radiology"},{"key":"e_1_2_12_17_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104774","article-title":"AugMS\u2010Net: Augmented Multiscale Network for Small Cervical Tumor Segmentation From MRI Volumes","volume":"141","author":"Lu P.","year":"2022","journal-title":"Computers in Biology and Medicine"},{"issue":"21","key":"e_1_2_12_18_1","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ad84b2","article-title":"Dual Convolution\u2010Transformer UNet (DCT\u2010UNet) for Organs at Risk and Clinical Target Volume Segmentation in MRI for Cervical Cancer Brachytherapy","volume":"69","author":"Kim G.","year":"2024","journal-title":"Physics in Medicine & Biology"},{"issue":"9","key":"e_1_2_12_19_1","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.13725","article-title":"Fully Automated Segmentation of Clinical Target Volume in Cervical Cancer From Magnetic Resonance Imaging With Convolutional Neural Network","volume":"23","author":"Zabihollahy F.","year":"2022","journal-title":"Journal of Applied Clinical Medical Physics"},{"issue":"3","key":"e_1_2_12_20_1","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1002\/mp.15490","article-title":"Automatic Segmentation of High\u2010Risk Clinical Target Volume for Tandem\u2010and\u2010Ovoids Brachytherapy Patients Using an Asymmetric Dual\u2010Path Convolutional Neural Network","volume":"49","author":"Cao Y.","year":"2022","journal-title":"Medical Physics"},{"issue":"11","key":"e_1_2_12_21_1","doi-asserted-by":"crossref","first-page":"7028","DOI":"10.1002\/mp.15268","article-title":"Fully Automated Multiorgan Segmentation of Female Pelvic Magnetic Resonance Images With Coarse\u2010to\u2010Fine Convolutional Neural Network","volume":"48","author":"Zabihollahy F.","year":"2021","journal-title":"Medical Physics"},{"issue":"6","key":"e_1_2_12_22_1","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1109\/TMI.2012.2186976","article-title":"Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy","volume":"31","author":"Lu C.","year":"2012","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_2_12_23_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.phro.2024.100578","article-title":"Automatic Gross Tumor Volume Segmentation With Failure Detection for Safe Implementation in Locally Advanced Cervical Cancer","volume":"30","author":"Rouhi R.","year":"2024","journal-title":"Physics and Imaging in Radiation Oncology"},{"issue":"6","key":"e_1_2_12_24_1","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1080\/0284186X.2017.1285499","article-title":"Autodelineation of Cervical Cancers Using Multiparametric Magnetic Resonance Imaging and Machine Learning","volume":"56","author":"Torheim T.","year":"2017","journal-title":"Acta Oncologica"},{"key":"e_1_2_12_25_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.phro.2024.100669","article-title":"Evaluation of Deep Learning\u2010Based Target Auto\u2010Segmentation for Magnetic Resonance Imaging\u2010Guided Cervix Brachytherapy","volume":"32","author":"Sim\u00f5es R.","year":"2024","journal-title":"Physics and Imaging in Radiation Oncology"},{"issue":"1","key":"e_1_2_12_26_1","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s13244-022-01356-8","article-title":"Generalizable Transfer Learning of Automated Tumor Segmentation From Cervical Cancers Toward a Universal Model for Uterine Malignancies in Diffusion\u2010Weighted MRI","volume":"14","author":"Lin Y.\u2010C.","year":"2023","journal-title":"Insights Into Imaging"},{"issue":"2","key":"e_1_2_12_27_1","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1007\/s10278-023-00951-5","article-title":"Reliable Delineation of Clinical Target Volumes for Cervical Cancer Radiotherapy on Ct\/Mr Dual\u2010Modality Images","volume":"37","author":"Sun Y.","year":"2024","journal-title":"Journal of Imaging Informatics in Medicine"},{"issue":"5","key":"e_1_2_12_28_1","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1002\/jmri.21500","article-title":"Iterative Active Deformational Methodology for Tumor Delineation: Evaluation Across Radiation Treatment Stage and Volume","volume":"28","author":"Wu D.","year":"2008","journal-title":"Journal of Magnetic Resonance Imaging"},{"issue":"1","key":"e_1_2_12_29_1","article-title":"Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm","volume":"2021","author":"Wang B.","year":"2021","journal-title":"Contrast Media & Molecular Imaging"},{"key":"e_1_2_12_30_1","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.854349","article-title":"Deep Learning for Per\u2010Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer","volume":"12","author":"Breto A. L.","year":"2022","journal-title":"Frontiers in Oncology"},{"issue":"10","key":"e_1_2_12_31_1","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.3390\/cancers14102372","article-title":"Fully Automatic Whole\u2010Volume Tumor Segmentation in Cervical Cancer","volume":"14","author":"Hodneland E.","year":"2022","journal-title":"Cancers"},{"issue":"5","key":"e_1_2_12_32_1","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1093\/jrr\/rrab070","article-title":"Automatic Contour Segmentation of Cervical Cancer Using Artificial Intelligence","volume":"62","author":"Kano Y.","year":"2021","journal-title":"Journal of Radiation Research"},{"issue":"3","key":"e_1_2_12_33_1","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1007\/s13246-024-01415-y","article-title":"Automatic Segmentation of Tumour and Organs at Risk in 3D MRI for Cervical Cancer Radiation Therapy With Anatomical Variations","volume":"47","author":"Leung S.\u2010N.","year":"2024","journal-title":"Physical and Engineering Sciences in Medicine"},{"issue":"6","key":"e_1_2_12_34_1","doi-asserted-by":"crossref","DOI":"10.1007\/s10916-019-1284-7","article-title":"Application of Deep Learning Algorithm in Cervical Cancer MRI Image Segmentation Based on Wireless Sensor","volume":"43","author":"Liang P.","year":"2019","journal-title":"Journal of Medical Systems"},{"issue":"1","key":"e_1_2_12_35_1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1186\/s13014-025-02664-1","article-title":"Automatic Cervical Tumors Segmentation in PET\/MRI by Parallel Encoder U\u2010Net","volume":"20","author":"Liu S.","year":"2025","journal-title":"Radiation Oncology"},{"key":"e_1_2_12_36_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2024.110332","article-title":"Generalizability of Deep Learning in Organ\u2010At\u2010Risk Segmentation: A Transfer Learning Study in Cervical Brachytherapy","volume":"197","author":"Ni R.","year":"2024","journal-title":"Radiotherapy and Oncology"},{"key":"e_1_2_12_37_1","doi-asserted-by":"crossref","DOI":"10.1002\/mp.17840","article-title":"Geometrically Focused Training and Evaluation of Organs\u2010at\u2010Risk Segmentation via Deep Learning","volume":"52","author":"Ni R.","year":"2025","journal-title":"Medical Physics"},{"issue":"4","key":"e_1_2_12_38_1","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.ijrobp.2020.10.038","article-title":"Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer","volume":"109","author":"Rigaud B.","year":"2021","journal-title":"International Journal of Radiation Oncology, Biology, Physics"},{"issue":"1","key":"e_1_2_12_39_1","doi-asserted-by":"crossref","DOI":"10.1186\/s13014-023-02283-8","article-title":"Deep Learning for Segmentation of the Cervical Cancer Gross Tumor Volume on Magnetic Resonance Imaging for Brachytherapy","volume":"18","author":"Rodr\u00edguez Outeiral R.","year":"2023","journal-title":"Radiation Oncology"},{"key":"e_1_2_12_40_1","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1007\/s10278-025-01464-z","article-title":"Application of TransUnet Deep Learning Model for Automatic Segmentation of Cervical Cancer in Small\u2010Field T2WI Images","volume":"38","author":"Shi Z.","year":"2025","journal-title":"Journal of Imaging Informatics in Medicine"},{"issue":"1","key":"e_1_2_12_41_1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s41747-025-00557-2","article-title":"Cer\u2010ConvN3Unet: An End\u2010To\u2010End Multi\u2010Parametric MRI\u2010Based Pipeline for Automated Detection and Segmentation of Cervical Cancer","volume":"9","author":"Xia S.\u2010J.","year":"2025","journal-title":"European Radiology Experimental"},{"issue":"19","key":"e_1_2_12_42_1","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ad7ad1","article-title":"Deep Learning\u2010Based Segmentation for High\u2010Dose\u2010Rate Brachytherapy in Cervical Cancer Using 3D Prompt\u2010ResUNet","volume":"69","author":"Xue X.","year":"2024","journal-title":"Physics in Medicine & Biology"},{"issue":"3","key":"e_1_2_12_43_1","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1002\/mp.15506","article-title":"Automatic Segmentation of Magnetic Resonance Images for High\u2010Dose\u2010Rate Cervical Cancer Brachytherapy Using Deep Learning","volume":"49","author":"Yoganathan S.","year":"2022","journal-title":"Medical Physics"},{"issue":"8","key":"e_1_2_12_44_1","doi-asserted-by":"crossref","first-page":"5408","DOI":"10.21037\/qims-24-560","article-title":"Integrating a Deep Neural Network and Transformer Architecture for the Automatic Segmentation and Survival Prediction in Cervical Cancer","volume":"14","author":"Zhu S.","year":"2024","journal-title":"Quantitative Imaging in Medicine and Surgery"}],"container-title":["Journal of Medical Radiation Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmrs.70127","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jmrs.70127","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmrs.70127","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:21:25Z","timestamp":1790572885000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jmrs.70127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,27]]},"references-count":43,"alternative-id":["10.1002\/jmrs.70127"],"URL":"https:\/\/doi.org\/10.1002\/jmrs.70127","archive":["Portico"],"relation":{},"ISSN":["2051-3895","2051-3909"],"issn-type":[{"value":"2051-3895","type":"print"},{"value":"2051-3909","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,27]]},"assertion":[{"value":"2026-07-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"jmrs.70127"}}