{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:15:29Z","timestamp":1787062529972,"version":"build-2736575974"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"European Union\u2019s Horizon 2020","award":["857894"],"award-info":[{"award-number":["857894"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-14265-9","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:16:49Z","timestamp":1768285009000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving rectal tumor segmentation with anomaly fusion derived from anatomical inpainting: a multicenter study"],"prefix":"10.1038","volume":"16","author":[{"given":"Lishan","family":"Cai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed A.","family":"Abdelatty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyi","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doenja M. J.","family":"Lambregts","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joost","family":"van Griethuysen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"Pooch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Regina G. H.","family":"Beets-Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Benson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joren","family":"Brunekreef","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Teuwen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"14265_CR1","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1007\/s00330-017-5026-2","volume":"28","author":"RG Beets-Tan","year":"2018","unstructured":"Beets-Tan, R. G. et al. Magnetic resonance imaging for clinical management of rectal cancer: updated recommendations from the 2016 European society of Gastrointestinal and abdominal radiology (ESGAR) consensus meeting. Eur. Radiol. 28, 1465\u20131475 (2018).","journal-title":"Eur. Radiol."},{"key":"14265_CR2","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1148\/radiol.2431051825","volume":"243","author":"MERCURY Study Group","year":"2007","unstructured":"MERCURY Study Group. Extramural depth of tumor invasion at thin-section MR in patients with rectal cancer: results of the MERCURY study. Radiology 243, 132\u2013139 (2007).","journal-title":"Radiology"},{"key":"14265_CR3","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1148\/rg.2019180114","volume":"39","author":"N Horvat","year":"2019","unstructured":"Horvat, N., Rocha, C. T., Clemente Oliveira, C., Petkovska, B., Gollub, M.   J. MRI of rectal cancer: tumor staging, imaging techniques, and management. Radiographics 39, 367\u2013387 (2019).","journal-title":"Radiographics"},{"key":"14265_CR4","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1186\/1477-7819-6-89","volume":"6","author":"C Suzuki","year":"2008","unstructured":"Suzuki, C. et al. The importance of rectal cancer MRI protocols on iInterpretation accuracy. World J. Surg. Onc. 6, 89 (2008).","journal-title":"World J. Surg. Onc"},{"key":"14265_CR5","doi-asserted-by":"publisher","first-page":"20200543","DOI":"10.1259\/bjr.20200543","volume":"93","author":"N Hearn","year":"2020","unstructured":"Hearn, N. et al. Manual and semi-automated delineation of locally advanced rectal cancer subvolumes with diffusion-weighted MRI. Br. J. Radiol. 93, 20200543 (2020).","journal-title":"Br. J. Radiol."},{"key":"14265_CR6","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.media.2016.03.002","volume":"32","author":"B Irving","year":"2016","unstructured":"Irving, B. et al. Pieces-of-parts for supervoxel segmentation with global context: application to DCE-MRI tumour delineation. Med. Image. Anal. 32, 69\u201383 (2016).","journal-title":"Med. Image. Anal."},{"key":"14265_CR7","doi-asserted-by":"publisher","first-page":"5301","DOI":"10.1038\/s41598-017-05728-9","volume":"7","author":"S Trebeschi","year":"2017","unstructured":"Trebeschi, S. et al. Deep learning for Fully-Automated localization and segmentation of rectal cancer on multiparametric MR. Sci. Rep. 7, 5301 (2017).","journal-title":"Sci. Rep."},{"key":"14265_CR8","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/978-3-319-65981-7_12","volume-title":"Classification in BioApps: Automation of Decision Making","author":"MI Razzak","year":"2018","unstructured":"Razzak, M. I., Naz, S. & Zaib, A. Deep learning for medical image processing: overview, challenges and the future. In Classification in BioApps: Automation of Decision Making (eds. Dey, N., Ashour, A. S. & Borra, S.) 323\u2013350 (Springer International Publishing, 2018). https:\/\/doi.org\/10.1007\/978-3-319-65981-7_12."},{"key":"14265_CR9","first-page":"234","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015 (eds. Navab, N. et al.) vol. 9351 234\u2013241 (Springer International Publishing, 2015)."},{"key":"14265_CR10","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s13246-018-0636-9","volume":"41","author":"J Jian","year":"2018","unstructured":"Jian, J. et al. Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images. Australas Phys. Eng. Sci. Med. 41, 393\u2013401 (2018).","journal-title":"Australas Phys. Eng. Sci. Med."},{"key":"14265_CR11","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1080\/0284186X.2021.2013530","volume":"61","author":"F Knuth","year":"2022","unstructured":"Knuth, F. et al. MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts. Acta Oncol. 61, 255\u2013263 (2022).","journal-title":"Acta Oncol."},{"key":"14265_CR12","doi-asserted-by":"publisher","first-page":"2560","DOI":"10.1002\/mp.12918","volume":"45","author":"J Wang","year":"2018","unstructured":"Wang, J. et al. Technical note: a deep learning-based autosegmentation of rectal tumors in MR images. Med. Phys. 45, 2560\u20132564 (2018).","journal-title":"Med. Phys."},{"key":"14265_CR13","doi-asserted-by":"crossref","unstructured":"Mlynarski, P., Delingette, H., Criminisi, A. & Ayache, N. 3D convolutional neural networks for tumor segmentation using long-range 2D context. Comput. Med. Imaging Graph. 73, 60\u201372 (2019).","DOI":"10.1016\/j.compmedimag.2019.02.001"},{"key":"14265_CR14","doi-asserted-by":"publisher","first-page":"e0269931","DOI":"10.1371\/journal.pone.0269931","volume":"17","author":"A Hamabe","year":"2022","unstructured":"Hamabe, A. et al. Artificial intelligence\u2013based technology for semi-automated segmentation of rectal cancer using high-resolution MRI. PLoS One. 17, e0269931 (2022).","journal-title":"PLoS One"},{"key":"14265_CR15","doi-asserted-by":"publisher","first-page":"104791","DOI":"10.1016\/j.bspc.2023.104791","volume":"84","author":"H Xiao","year":"2023","unstructured":"Xiao, H., Li, L., Liu, Q., Zhu, X. & Zhang, Q. Transformers in medical image segmentation: a review. Biomed. Signal Process. Control. 84, 104791 (2023).","journal-title":"Biomed. Signal Process. Control"},{"key":"14265_CR16","doi-asserted-by":"publisher","first-page":"107842","DOI":"10.1016\/j.cmpb.2023.107842","volume":"242","author":"D Li","year":"2023","unstructured":"Li, D. et al. RTAU-Net: a novel 3D rectal tumor segmentation model based on dual path fusion and attentional guidance. Comput. Methods Programs Biomed. 242, 107842 (2023).","journal-title":"Comput. Methods Programs Biomed."},{"key":"14265_CR17","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J. & Maier-Hein, K. H. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods. 18, 203\u2013211 (2021).","journal-title":"Nat. Methods"},{"key":"14265_CR18","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","volume":"32","author":"HY Zhou","year":"2023","unstructured":"Zhou, H. Y. et al. NnFormer: volumetric medical image segmentation via a 3D transformer. IEEE Trans. Image Process. 32, 4036\u20134045 (2023).","journal-title":"IEEE Trans. Image Process."},{"key":"14265_CR19","doi-asserted-by":"crossref","unstructured":"DeSilvio, T. et al. Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study. Front. Med. 10, 1422 (2023).","DOI":"10.3389\/fmed.2023.1149056"},{"key":"14265_CR20","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1002\/ima.22311","volume":"29","author":"J Kim","year":"2019","unstructured":"Kim, J. et al. Rectal cancer: toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network. Int. J. Imaging Syst. Technol. 29, 247\u2013259 (2019).","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"14265_CR21","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1002\/path.1621","volume":"204","author":"I Nagtegaal","year":"2004","unstructured":"Nagtegaal, I. et al. Morphological changes in tumour type after radiotherapy are accompanied by changes in gene expression profile but not in clinical behaviour. J. Pathol. 204, 183\u2013192 (2004).","journal-title":"J. Pathol."},{"key":"14265_CR22","doi-asserted-by":"publisher","first-page":"182725","DOI":"10.1109\/ACCESS.2019.2960371","volume":"7","author":"J Lee","year":"2019","unstructured":"Lee, J., Oh, J. E., Kim, M. J., Hur, B. Y. & Sohn, D. K. Reducing the model variance of a rectal cancer segmentation network. IEEE Access. 7, 182725\u2013182733 (2019).","journal-title":"IEEE Access."},{"key":"14265_CR23","unstructured":"Goodfellow, I. et al. Generative adversarial Nets. In Advances in Neural Information Processing Systems vol. 27 (Curran Associates, Inc., 2014)."},{"key":"14265_CR24","unstructured":"Kingma, D. P. & Welling, M. Auto-encoding variational bayes  (2022). http:\/\/arxiv.org\/abs\/1312.6114."},{"key":"14265_CR25","unstructured":"van den Oord, A. & Vinyals, O. & kavukcuoglu, koray. Neural discrete representation learning. In Advances in Neural Information Processing Systems vol. 30 (Curran Associates, Inc., 2017)."},{"key":"14265_CR26","doi-asserted-by":"publisher","first-page":"101952","DOI":"10.1016\/j.media.2020.101952","volume":"69","author":"C Baur","year":"2021","unstructured":"Baur, C., Denner, S., Wiestler, B., Navab, N. & Albarqouni, S. Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med. Image Anal. 69, 101952 (2021).","journal-title":"Med. Image Anal."},{"key":"14265_CR27","doi-asserted-by":"crossref","unstructured":"Chen, X., You, S., Tezcan, K. C. & Konukoglu, E. Unsupervised lesion detection via image restoration with a normative prior  (2020). http:\/\/arxiv.org\/abs\/2005.00031.","DOI":"10.1016\/j.media.2020.101713"},{"key":"14265_CR28","doi-asserted-by":"publisher","first-page":"102475","DOI":"10.1016\/j.media.2022.102475","volume":"79","author":"WHL Pinaya","year":"2022","unstructured":"Pinaya, W. H. L. et al. Unsupervised brain imaging 3D anomaly detection and segmentation with Transformers. Med. Image Anal. 79, 102475 (2022).","journal-title":"Med. Image Anal."},{"key":"14265_CR29","doi-asserted-by":"publisher","unstructured":"Nguyen, B., Feldman, A., Bethapudi, S., Jennings, A. & Willcocks, C. G. Unsupervised region-based anomaly detection In brain MRI With adversarial image inpainting. In IEEE 18th International Symposium on Biomedical Imaging (ISBI) 1127\u20131131 (2021).  (2021). https:\/\/doi.org\/10.1109\/ISBI48211.2021.9434115.","DOI":"10.1109\/ISBI48211.2021.9434115"},{"key":"14265_CR30","unstructured":"Yeganeh, Y., Farshad, A. & Navab, N. Shape-aware masking for inpainting in medical imaging  (2022). http:\/\/arxiv.org\/abs\/2207.05787."},{"key":"14265_CR31","doi-asserted-by":"publisher","first-page":"103089","DOI":"10.1016\/j.media.2024.103089","volume":"93","author":"B Woo","year":"2024","unstructured":"Woo, B. et al. Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the osteoarthritis initiative. Med. Image Anal. 93, 103089 (2024).","journal-title":"Med. Image Anal."},{"key":"14265_CR32","unstructured":"Saha, A. et al. Artificial intelligence and radiologists at prostate cancer detection in MRI \u2014 The PI-CAI challenge  (2023)."},{"key":"14265_CR33","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging. 34, 1993\u20132024 (2015).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"14265_CR34","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/s00261-021-03281-8","volume":"47","author":"N Bogveradze","year":"2022","unstructured":"Bogveradze, N. et al. Evolutions in rectal cancer MRI staging and risk stratification in the Netherlands. Abdom. Radiol. 47, 38\u201347 (2022).","journal-title":"Abdom. Radiol."},{"key":"14265_CR35","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1007\/s00330-021-08251-8","volume":"32","author":"NW Schurink","year":"2022","unstructured":"Schurink, N. W. et al. Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility. Eur. Radiol. 32, 1506\u20131516 (2022).","journal-title":"Eur. Radiol."},{"key":"14265_CR36","unstructured":"Schurink, N. W. et al. Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer. Eur. Radiol. 2023, 1\u201310 (2023)."},{"key":"14265_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41698-024-00516-x","volume":"8","author":"L Cai","year":"2024","unstructured":"Cai, L. et al. An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study. Npj Precis Onc. 8, 1\u201311 (2024).","journal-title":"Npj Precis Onc"},{"key":"14265_CR38","doi-asserted-by":"publisher","first-page":"3039","DOI":"10.1007\/s00261-023-03961-7","volume":"48","author":"N El Khababi","year":"2023","unstructured":"El Khababi, N. et al. Predicting response to chemoradiotherapy in rectal cancer via visual morphologic assessment and staging on baseline MRI: a multicenter and multireader study. Abdom. Radiol. 48, 3039\u20133049 (2023).","journal-title":"Abdom. Radiol."},{"key":"14265_CR39","doi-asserted-by":"crossref","unstructured":"ESGAR. Book of Abstracts. Insights into Imaging 11, 64 (2020).","DOI":"10.1186\/s13244-020-00873-8"},{"key":"14265_CR40","unstructured":"FACG, M. A. M. MD, FACR. Dynamic Radiology of the Abdomen: Normal and Pathologic Anatomy (Springer Science & Business Media, 2006)."},{"key":"14265_CR41","doi-asserted-by":"publisher","unstructured":"Lambregts, D. M. J., Maas, M. & Beets-Tan, R. G. H. MRI of the Rectum. In MRI of the Gastrointestinal Tract (ed. Stoker, J.) 205\u2013227 (Springer, 2010). https:\/\/doi.org\/10.1007\/978-3-540-85532-3_13.","DOI":"10.1007\/978-3-540-85532-3_13"},{"key":"14265_CR42","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s13244-022-01348-8","volume":"14","author":"N Bogveradze","year":"2023","unstructured":"Bogveradze, N. et al. MRI anatomy of the rectum: key concepts important for rectal cancer staging and treatment planning. Insights into Imaging. 14, 13 (2023).","journal-title":"Insights into Imaging"},{"key":"14265_CR43","doi-asserted-by":"publisher","first-page":"103044","DOI":"10.1016\/j.media.2023.103044","volume":"92","author":"L Han","year":"2024","unstructured":"Han, L. et al. Synthesis-based imaging-differentiation representation learning for multi-sequence 3D\/4D MRI. Med. Image Anal. 92, 103044 (2024).","journal-title":"Med. Image Anal."},{"key":"14265_CR44","doi-asserted-by":"publisher","unstructured":"Han, L. et al. An explainable deep framework: towards Task-Specific fusion for Multi-to-One MRI synthesis. In Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023 (ed. Greenspan, H.) 45\u201355 (Springer Nature Switzerland, C2023). https:\/\/doi.org\/10.1007\/978-3-031-43999-5_5.","DOI":"10.1007\/978-3-031-43999-5_5"},{"key":"14265_CR45","doi-asserted-by":"publisher","unstructured":"Johnson, J., Alahi, A. & Fei-Fei, L. Perceptual losses for Real-Time style transfer and Super-Resolution. In Computer Vision \u2013 ECCV 2016 (eds. Leibe, B. et al.) 694\u2013711 (Springer International Publishing, 2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"14265_CR46","doi-asserted-by":"publisher","unstructured":"Zhu, J. Y., Park, T., Isola, P. & EfrosA. A. Unpaired Image-to-Image translation using Cycle-Consistent adversarial networks. In 2017 IEEE Int. Conf. Comput. Vis. (ICCV) 2242-2251  (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.244.","DOI":"10.1109\/ICCV.2017.244"},{"key":"14265_CR47","doi-asserted-by":"publisher","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016 (eds. Ourselin, S. et al.) 424\u2013432 (Springer International Publishing, 2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"14265_CR48","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis, F. I., Waldner, F., Caccetta, P. & Wu, C. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J. Photogrammetry Remote Sens. 162, 94\u2013114 (2020).","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"14265_CR49","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A. et al. UNETR: Transformers for 3D medical image segmentation 574\u2013584 (2022).","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"14265_CR50","doi-asserted-by":"publisher","unstructured":"Hatamizadeh, A. et al. Swin UNETR: Swin Transformers for semantic segmentation of brain tumors in MRI images. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (eds. Crimi, A. & Bakas, S.) 272\u2013284 (Springer International Publishing, 2022). doi:https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"14265_CR51","doi-asserted-by":"publisher","unstructured":"Oktay, O. et al. Attention U-Net: learning where to look for the pancreas  (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.03999.","DOI":"10.48550\/arXiv.1804.03999"},{"key":"14265_CR52","doi-asserted-by":"publisher","unstructured":"Gao, Y. et al. A Data-scalable transformer for medical image segmentation: architecture, model efficiency, and benchmark  (2023). https:\/\/doi.org\/10.48550\/arXiv.2203.00131.","DOI":"10.48550\/arXiv.2203.00131"},{"key":"14265_CR53","doi-asserted-by":"publisher","unstructured":"Ma, J., Li, F. & Wang, B. U-Mamba: enhancing long-range dependency for biomedical image segmentation (2024). https:\/\/doi.org\/10.48550\/arXiv.2401.04722.","DOI":"10.48550\/arXiv.2401.04722"},{"key":"14265_CR54","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Identity mappings in deep residual networks. In Computer Vision \u2013 ECCV 2016 (eds. Leibe, B. et al.) 630\u2013645 (Springer International Publishing, 2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"14265_CR55","unstructured":"Gu, A. et al.   Combining recurrent, convolutional, and continuous-time models with linear state space layers. In Advances in Neural Information Processing Systems,  vol. 34 572\u2013585 (2021)."},{"key":"14265_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-022-00592-y","volume":"5","author":"G Varoquaux","year":"2022","unstructured":"Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. Npj Digit. Med. 5, 1\u20138 (2022).","journal-title":"Npj Digit. Med."},{"key":"14265_CR57","unstructured":"Teney, D. et al. On the value of out-of-distribution testing: an example of Goodhart\u2019 s Law. In Advances in Neural Information Processing Systems,  vol. 33  407\u2013417 (2020)."},{"key":"14265_CR58","doi-asserted-by":"crossref","unstructured":"Isensee, F. et al. nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation  (2024). http:\/\/arxiv.org\/abs\/2404.09556.","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"14265_CR59","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1148\/radiol.2020192224","volume":"295","author":"MJ Willemink","year":"2020","unstructured":"Willemink, M. J. et al. Preparing medical imaging data for machine learning. Radiology 295, 4\u201315 (2020).","journal-title":"Radiology"},{"key":"14265_CR60","unstructured":"Jayaprakasam, V. S. et al. MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer. Eur. Radiol. 2022, 1\u201310 (2022)."},{"key":"14265_CR61","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s41747-022-00272-2","volume":"6","author":"A Defeudis","year":"2022","unstructured":"Defeudis, A. et al. MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study. Eur. Radiol. Exp.. 6, 19 (2022).","journal-title":"Eur. Radiol. Exp."},{"key":"14265_CR62","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1007\/s11517-023-02828-9","volume":"61","author":"M Dou","year":"2023","unstructured":"Dou, M. et al. Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network. Med. Biol. Eng. Comput. 61, 2379\u20132389 (2023).","journal-title":"Med. Biol. Eng. Comput."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-14265-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-14265-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-14265-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T23:03:04Z","timestamp":1768345384000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-14265-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,13]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["14265"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-14265-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,13]]},"assertion":[{"value":"26 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1545"}}