{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T18:04:03Z","timestamp":1793988243514,"version":"4.8.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Capital's Funds for Health Improvement and Research","award":["2024-1-1022"],"award-info":[{"award-number":["2024-1-1022"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2023YFC3402805"],"award-info":[{"award-number":["2023YFC3402805"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science Foundation of Peking University Cancer Hospital","award":["XKFZ2403"],"award-info":[{"award-number":["XKFZ2403"]}],"id":[{"id":"https:\/\/ror.org\/00nyxxr91","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol Exp"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>We established and validated an innovative two-phase pipeline for automated detection and segmentation on multi-parametric cervical cancer magnetic resonance imaging (MRI) and investigated the clinical efficacy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The retrospective multicenter study included 125 cervical cancer patients enrolled in two hospitals for 14,547 two-dimensional images. All the patients underwent pelvic MRI examinations consisting of diffusion-weighted imaging (DWI), T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI). The deep learning framework involved a multiparametric detection module utilizing ConvNeXt blocks and a subsequent segmentation module utilizing 3-channel DoubleU-Nets. The pipeline was trained and tested (80:20 ratio) on 3,077 DWI, 2,990 T2WI, and 8,480 CE-T1WI slices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In terms of reference standards from gynecologic radiologists, the first automated detection module achieved overall results of 93% accuracy (95% confidence interval 92\u201394%), 93% precision (92\u201394%), 93% recall (92\u201394%), 0.90 \u03ba (0.89\u20130.91), and 0.93 F1-score (0.92\u20130.94). The second-stage segmentation exhibited Dice similarity coefficients and Jaccard values of 83% (81\u201385%) and 71% (69\u201374%) for DWI, 79% (75\u201382%), and 65% (61\u201369%) for T2WI, 74% (71\u201376%) and 59% (56\u201362%) for CE-T1WI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Independent experiments demonstrated that the pipeline could get high recognition and segmentation accuracy without human intervention, thus effectively reducing the delineation burden for radiologists and gynecologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>The proposed pipeline is potentially an alternative tool in imaging reading and processing cervical cancer. Meanwhile, this can serve as the basis for subsequent work related to tumor lesions. The pipeline contributes to saving the working time of radiologists and gynecologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>An AI-assisted multiparametric MRI-based pipeline can effectively support radiologists in cervical cancer evaluation.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The proposed pipeline shows high recognition and segmentation performance without manual intervention.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The proposed pipeline may become a promising auxiliary tool in gynecological imaging.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-025-00557-2","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T12:08:04Z","timestamp":1739880484000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Cer-ConvN3Unet: an end-to-end multi-parametric MRI-based pipeline for automated detection and segmentation of cervical cancer"],"prefix":"10.1186","volume":"9","author":[{"given":"Shao-Jun","family":"Xia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxing","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Nan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingmo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Qi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haijiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai-Tao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Ting","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Yan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9424-1910","authenticated-orcid":false,"given":"Ying-Shi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"557_CR1","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1016\/S0140-6736(07)61416-0","volume":"370","author":"M Schiffman","year":"2007","unstructured":"Schiffman M, Castle PE, Jeronimo J, Rodriguez AC, Wacholder S (2007) Human papillomavirus and cervical cancer. Lancet 370:890\u2013907. https:\/\/doi.org\/10.1016\/S0140-6736(07)61416-0","journal-title":"Lancet"},{"key":"557_CR2","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/S0140-6736(18)32470-X","volume":"393","author":"PA Cohen","year":"2019","unstructured":"Cohen PA, Jhingran A, Oaknin A, Denny L (2019) Cervical cancer. Lancet 393:169\u2013182. https:\/\/doi.org\/10.1016\/S0140-6736(18)32470-X","journal-title":"Lancet"},{"key":"557_CR3","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209\u2013249. https:\/\/doi.org\/10.3322\/caac.21660","journal-title":"CA Cancer J Clin"},{"key":"557_CR4","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1007\/s00330-010-1998-x","volume":"21","author":"C Balleyguier","year":"2011","unstructured":"Balleyguier C, Sala E, Da Cunha T et al (2011) Staging of uterine cervical cancer with MRI: guidelines of the European Society of Urogenital Radiology. Eur Radiol 21:1102\u20131110. https:\/\/doi.org\/10.1007\/s00330-010-1998-x","journal-title":"Eur Radiol"},{"key":"557_CR5","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak HJR (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563\u2013577. https:\/\/doi.org\/10.1148\/radiol.2015151169","journal-title":"Radiology"},{"key":"557_CR6","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1148\/radiol.2021202553","volume":"298","author":"MR Tomaszewski","year":"2021","unstructured":"Tomaszewski MR, Gillies RJ (2021) The biological meaning of radiomic features. Radiology 298:505\u2013516. https:\/\/doi.org\/10.1148\/radiol.2021202553","journal-title":"Radiology"},{"key":"557_CR7","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1002\/mp.13264","volume":"46","author":"B Sahiner","year":"2019","unstructured":"Sahiner B, Pezeshk A, Hadjiiski LM et al (2019) Deep learning in medical imaging and radiation therapy. Med Phys 46:e1\u2013e36. https:\/\/doi.org\/10.1002\/mp.13264","journal-title":"Med Phys"},{"key":"557_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-022-02148-6","volume":"17","author":"C Yang","year":"2022","unstructured":"Yang C, Qin L-H, Xie Y-E, Liao J-Y (2022) Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis. Radiat Oncol 17:175. https:\/\/doi.org\/10.1186\/s13014-022-02148-6","journal-title":"Radiat Oncol"},{"key":"557_CR9","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-023-02283-8","volume":"18","author":"R Rodr\u00edguez Outeiral","year":"2023","unstructured":"Rodr\u00edguez Outeiral R, Gonz\u00e1lez PJ, Schaake EE, van der Heide UA, Sim\u00f5es R (2023) Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy. Radiat Oncol 18:91. https:\/\/doi.org\/10.1186\/s13014-023-02283-8","journal-title":"Radiat Oncol"},{"key":"557_CR10","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.ygyno.2007.06.026","volume":"107","author":"L-C Horn","year":"2007","unstructured":"Horn L-C, Fischer U, Raptis G, Bilek K, Hentschel B (2007) Tumor size is of prognostic value in surgically treated FIGO stage II cervical cancer. Gynecol Oncol 107:310\u2013315. https:\/\/doi.org\/10.1016\/j.ygyno.2007.06.026","journal-title":"Gynecol Oncol"},{"key":"557_CR11","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.3389\/fonc.2020.01393","volume":"10","author":"L Hou","year":"2020","unstructured":"Hou L, Zhou W, Ren J et al (2020) Radiomics analysis of multiparametric MRI for the preoperative prediction of lymph node metastasis in cervical cancer. Front Oncol 10:1393. https:\/\/doi.org\/10.3389\/fonc.2020.01393","journal-title":"Front Oncol"},{"key":"557_CR12","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-022-00474-2","volume":"22","author":"X Zhang","year":"2022","unstructured":"Zhang X, Zhao J, Zhang Q et al (2022) MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy. Cancer Imaging 22:35. https:\/\/doi.org\/10.1186\/s40644-022-00474-2","journal-title":"Cancer Imaging"},{"key":"557_CR13","doi-asserted-by":"publisher","first-page":"631","DOI":"10.3390\/diagnostics11040631","volume":"11","author":"B Gui","year":"2021","unstructured":"Gui B, Autorino R, Micc\u00f2 M et al (2021) Pretreatment MRI radiomics based response prediction model in locally advanced cervical cancer. Diagnostics 11:631. https:\/\/doi.org\/10.3390\/diagnostics11040631","journal-title":"Diagnostics"},{"key":"557_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.663370","volume":"11","author":"G Huang","year":"2022","unstructured":"Huang G, Cui Y, Wang P et al (2022) Multi-parametric magnetic resonance imaging-based radiomics analysis of cervical cancer for preoperative prediction of lymphovascular space invasion. Front Oncol 11:663370. https:\/\/doi.org\/10.3389\/fonc.2021.663370","journal-title":"Front Oncol"},{"key":"557_CR15","doi-asserted-by":"publisher","first-page":"312","DOI":"10.5152\/dir.2022.20657","volume":"28","author":"L Cui","year":"2022","unstructured":"Cui L, Yu T, Kan Y, Dong Y, Luo Y, Jiang X (2022) Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer. Diagn Interv Radiol 28:312. https:\/\/doi.org\/10.5152\/dir.2022.20657","journal-title":"Diagn Interv Radiol"},{"key":"557_CR16","doi-asserted-by":"publisher","first-page":"713","DOI":"10.21037\/atm.2020.02.44","volume":"8","author":"L Cai","year":"2020","unstructured":"Cai L, Gao J, Zhao D (2020) A review of the application of deep learning in medical image classification and segmentation. Ann Transl Med 8:713. https:\/\/doi.org\/10.21037\/atm.2020.02.44","journal-title":"Ann Transl Med"},{"key":"557_CR17","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.1109\/TPAMI.2021.3059968","volume":"44","author":"S Minaee","year":"2022","unstructured":"Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D (2022) Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell 44:3523\u20133542. https:\/\/doi.org\/10.1109\/TPAMI.2021.3059968","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"557_CR18","doi-asserted-by":"publisher","first-page":"5845","DOI":"10.1007\/s10462-022-10152-1","volume":"55","author":"NS Punn","year":"2022","unstructured":"Punn NS, Agarwal S (2022) Modality specific U-Net variants for biomedical image segmentation: a survey. Artif Intell Rev 55:5845\u20135889. https:\/\/doi.org\/10.1007\/s10462-022-10152-1","journal-title":"Artif Intell Rev"},{"key":"557_CR19","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1007\/s00234-020-02465-1","volume":"62","author":"P Windisch","year":"2020","unstructured":"Windisch P, Weber P, Furweger C et al (2020) Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices. Neuroradiology 62:1515\u20131518. https:\/\/doi.org\/10.1007\/s00234-020-02465-1","journal-title":"Neuroradiology"},{"key":"557_CR20","doi-asserted-by":"publisher","first-page":"034004","DOI":"10.1117\/1.JMI.3.3.034004","volume":"3","author":"M Paknezhad","year":"2016","unstructured":"Paknezhad M, Marchesseau S, Brown MS (2016) Automatic basal slice detection for cardiac analysis. J Med Imaging 3:034004. https:\/\/doi.org\/10.1117\/1.JMI.3.3.034004","journal-title":"J Med Imaging"},{"key":"557_CR21","doi-asserted-by":"publisher","first-page":"2372","DOI":"10.3390\/cancers14102372","volume":"14","author":"E Hodneland","year":"2022","unstructured":"Hodneland E, Kaliyugarasan S, Wagner-Larsen KS et al (2022) Fully automatic whole-volume tumor segmentation in cervical cancer. Cancers (Basel) 14:2372. https:\/\/doi.org\/10.3390\/cancers14102372","journal-title":"Cancers (Basel)"},{"key":"557_CR22","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1093\/jrr\/rrab070","volume":"62","author":"Y Kano","year":"2021","unstructured":"Kano Y, Ikushima H, Sasaki M, Haga A (2021) Automatic contour segmentation of cervical cancer using artificial intelligence. J Radiat Res 62:934\u2013944. https:\/\/doi.org\/10.1093\/jrr\/rrab070","journal-title":"J Radiat Res"},{"key":"557_CR23","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s00330-019-06467-3","volume":"30","author":"YC Lin","year":"2020","unstructured":"Lin YC, Lin CH, Lu HY et al (2020) Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer. Eur Radiol 30:1297\u20131305. https:\/\/doi.org\/10.1007\/s00330-019-06467-3","journal-title":"Eur Radiol"},{"key":"557_CR24","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.1007\/s10462-019-09735-2","volume":"53","author":"A Sarwar","year":"2019","unstructured":"Sarwar A, Sheikh AA, Manhas J, Sharma V (2019) Segmentation of cervical cells for automated screening of cervical cancer: a review. Artif Intell Rev 53:2341\u20132379. https:\/\/doi.org\/10.1007\/s10462-019-09735-2","journal-title":"Artif Intell Rev"},{"key":"557_CR25","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s13244-022-01356-8","volume":"14","author":"YC Lin","year":"2023","unstructured":"Lin YC, Lin Y, Huang YL et al (2023) Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights Imaging 14:14. https:\/\/doi.org\/10.1186\/s13244-022-01356-8","journal-title":"Insights Imaging"},{"key":"557_CR26","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1002\/mp.15506","volume":"49","author":"SA Yoganathan","year":"2022","unstructured":"Yoganathan SA, Paul SN, Paloor S et al (2022) Automatic segmentation of magnetic resonance images for high-dose-rate cervical cancer brachytherapy using deep learning. Med Phys 49:1571\u20131584. https:\/\/doi.org\/10.1002\/mp.15506","journal-title":"Med Phys"},{"key":"557_CR27","doi-asserted-by":"publisher","first-page":"e13725","DOI":"10.1002\/acm2.13725","volume":"23","author":"F Zabihollahy","year":"2022","unstructured":"Zabihollahy F, Viswanathan AN, Schmidt EJ, Lee J (2022) Fully automated segmentation of clinical target volume in cervical cancer from magnetic resonance imaging with convolutional neural network. J Appl Clin Med Phys 23:e13725. https:\/\/doi.org\/10.1002\/acm2.13725","journal-title":"J Appl Clin Med Phys"},{"key":"557_CR28","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/1673490","volume":"2021","author":"B Wang","year":"2021","unstructured":"Wang B, Zhang Y, Wu C, Wang F (2021) Multimodal MRI analysis of cervical cancer on the basis of artificial intelligence algorithm. Contrast Media Mol Imaging 2021:1673490. https:\/\/doi.org\/10.1155\/2021\/1673490","journal-title":"Contrast Media Mol Imaging"},{"key":"557_CR29","doi-asserted-by":"publisher","first-page":"854349","DOI":"10.3389\/fonc.2022.854349","volume":"12","author":"AL Breto","year":"2022","unstructured":"Breto AL, Spieler B, Zavala-Romero O et al (2022) Deep learning for per-fraction automatic segmentation of gross tumor volume (GTV) and organs at risk (OARs) in adaptive radiotherapy of cervical cancer. Front Oncol 12:854349. https:\/\/doi.org\/10.3389\/fonc.2022.854349","journal-title":"Front Oncol"},{"key":"557_CR30","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1080\/0284186X.2017.1285499","volume":"56","author":"T Torheim","year":"2017","unstructured":"Torheim T, Malinen E, Hole KH et al (2017) Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning. Acta Oncol 56:806\u2013812. https:\/\/doi.org\/10.1080\/0284186X.2017.1285499","journal-title":"Acta Oncol"},{"key":"557_CR31","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. IEEE, pp 11976\u201311986. https:\/\/doi.org\/10.48550\/arXiv.2201.03545","DOI":"10.48550\/arXiv.2201.03545"},{"key":"557_CR32","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1109\/TMI.2020.3046692","volume":"40","author":"Z Li","year":"2021","unstructured":"Li Z, Kamnitsas K, Glocker B (2021) Analyzing overfitting under class imbalance in neural networks for image segmentation. IEEE Trans Med Imaging 40:1065\u20131077. https:\/\/doi.org\/10.1109\/TMI.2020.3046692","journal-title":"IEEE Trans Med Imaging"},{"key":"557_CR33","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda M, Maki A, Mazurowski MA (2018) A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw 106:249\u2013259. https:\/\/doi.org\/10.1016\/j.neunet.2018.07.011","journal-title":"Neural Netw"},{"key":"557_CR34","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1109\/TMI.2003.815867","volume":"22","author":"JP Pluim","year":"2003","unstructured":"Pluim JP, Maintz JA, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22:986\u20131004. https:\/\/doi.org\/10.1109\/TMI.2003.815867","journal-title":"IEEE Trans Med Imaging"},{"key":"557_CR35","doi-asserted-by":"publisher","unstructured":"Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD (2020) DoubleU-Net: a deep convolutional neural network for medical image segmentation 2020 IEEE 33rd International Symposium on computer-based medical systems (CBMS). IEEE Computer Society, pp 558\u2013564. https:\/\/doi.org\/10.48550\/arXiv.2006.04868","DOI":"10.48550\/arXiv.2006.04868"},{"key":"557_CR36","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1016\/j.ygyno.2020.03.027","volume":"157","author":"PW Grigsby","year":"2020","unstructured":"Grigsby PW, Massad LS, Mutch DG et al (2020) FIGO 2018 staging criteria for cervical cancer: impact on stage migration and survival. Gynecol Oncol 157:639\u2013643. https:\/\/doi.org\/10.1016\/j.ygyno.2020.03.027","journal-title":"Gynecol Oncol"},{"key":"557_CR37","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-93792-7","volume":"11","author":"Y Kurata","year":"2021","unstructured":"Kurata Y, Nishio M, Moribata Y et al (2021) Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network. Sci Rep 11:14440. https:\/\/doi.org\/10.1038\/s41598-021-93792-7","journal-title":"Sci Rep"},{"key":"557_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2024.103385","volume":"122","author":"H Fu","year":"2024","unstructured":"Fu H, Zhang J, Li B et al (2024) Abdominal multi-organ segmentation in multi-sequence MRIs based on visual attention guided network and knowledge distillation. Phys Med 122:103385. https:\/\/doi.org\/10.1016\/j.ejmp.2024.103385","journal-title":"Phys Med"},{"key":"557_CR39","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1016\/j.ijrobp.2020.10.038","volume":"109","author":"B Rigaud","year":"2021","unstructured":"Rigaud B, Anderson BM, Yu ZH et al (2021) Automatic segmentation using deep learning to enable online dose optimization during adaptive radiation therapy of cervical cancer. Int J Radiat Oncol Biol Phys 109:1096\u20131110. https:\/\/doi.org\/10.1016\/j.ijrobp.2020.10.038","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"557_CR40","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1016\/j.radonc.2015.10.007","volume":"117","author":"G Eminowicz","year":"2015","unstructured":"Eminowicz G, McCormack M (2015) Variability of clinical target volume delineation for definitive radiotherapy in cervix cancer. Radiother Oncol 117:542\u2013547. https:\/\/doi.org\/10.1016\/j.radonc.2015.10.007","journal-title":"Radiother Oncol"},{"key":"557_CR41","doi-asserted-by":"publisher","first-page":"3968","DOI":"10.1002\/mp.14898","volume":"48","author":"J Shi","year":"2021","unstructured":"Shi J, Ding X, Liu X, Li Y, Liang W, Wu J (2021) Automatic clinical target volume delineation for cervical cancer in CT images using deep learning. Med Phys 48:3968\u20133981. https:\/\/doi.org\/10.1002\/mp.14898","journal-title":"Med Phys"},{"key":"557_CR42","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1148\/radiol.2017161299","volume":"284","author":"A Jalaguier-Coudray","year":"2017","unstructured":"Jalaguier-Coudray A, Villard-Mahjoub R, Delouche A et al (2017) Value of dynamic contrast-enhanced and diffusion-weighted MR imaging in the detection of pathologic complete response in cervical cancer after neoadjuvant therapy: a retrospective observational study. Radiology 284:432\u2013442. https:\/\/doi.org\/10.1148\/radiol.2017161299","journal-title":"Radiology"},{"key":"557_CR43","doi-asserted-by":"publisher","first-page":"7930","DOI":"10.1002\/mp.15285","volume":"48","author":"S Huang","year":"2021","unstructured":"Huang S, Cheng Z, Lai L et al (2021) Integrating multiple MRI sequences for pelvic organs segmentation via the attention mechanism. Med Phys 48:7930\u20137945. https:\/\/doi.org\/10.1002\/mp.15285","journal-title":"Med Phys"},{"key":"557_CR44","doi-asserted-by":"publisher","first-page":"e766","DOI":"10.1016\/j.radonc.2020.09.060","volume":"108","author":"L Zhikai","year":"2020","unstructured":"Zhikai L, Guan H (2020) Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy. Radiother Oncol 108:e766. https:\/\/doi.org\/10.1016\/j.radonc.2020.09.060","journal-title":"Radiother Oncol"},{"key":"557_CR45","doi-asserted-by":"publisher","first-page":"2557","DOI":"10.1007\/s00261-019-01984-7","volume":"44","author":"P Balcacer","year":"2019","unstructured":"Balcacer P, Shergill A, Litkouhi B (2019) MRI of cervical cancer with a surgical perspective: staging, prognostic implications and pitfalls. Abdom Radiol (NY) 44:2557\u20132571. https:\/\/doi.org\/10.1007\/s00261-019-01984-7","journal-title":"Abdom Radiol (NY)"},{"key":"557_CR46","doi-asserted-by":"publisher","first-page":"4360","DOI":"10.1016\/j.csbj.2022.08.023","volume":"20","author":"D Luo","year":"2022","unstructured":"Luo D, Kang H, Long J et al (2022) Dual supervised sampling networks for real-time segmentation of cervical cell nucleus. Comput Struct Biotechnol J 20:4360\u20134368. https:\/\/doi.org\/10.1016\/j.csbj.2022.08.023","journal-title":"Comput Struct Biotechnol J"},{"key":"557_CR47","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-95545-y","volume":"11","author":"C-W Wang","year":"2021","unstructured":"Wang C-W, Liou Y-A, Lin Y-J et al (2021) Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning. Sci Rep 11:16244. https:\/\/doi.org\/10.1038\/s41598-021-95545-y","journal-title":"Sci Rep"}],"container-title":["European Radiology Experimental"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s41747-025-00557-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s41747-025-00557-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s41747-025-00557-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T12:08:11Z","timestamp":1739880491000},"score":1,"resource":{"primary":{"URL":"https:\/\/eurradiolexp.springeropen.com\/articles\/10.1186\/s41747-025-00557-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,18]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["557"],"URL":"https:\/\/doi.org\/10.1186\/s41747-025-00557-2","relation":{},"ISSN":["2509-9280"],"issn-type":[{"value":"2509-9280","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,18]]},"assertion":[{"value":"28 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was conducted in accordance with the guidelines and regulations of the Declaration of Helsinki. All images used were acquired per clinical request and subsequently collected for research purposes with approval from the Ethics Committee of Peking University Hospital & Institute (20141208). The committee approved the retrospective analysis of the data and waived the requirement for informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"20"}}