{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T04:07:05Z","timestamp":1784088425727,"version":"3.55.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universitat de Valencia"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Colorectal Dis"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate identification of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) remains a key clinical challenge. Clinical complete response is an imperfect surrogate, and pCR can only be definitively established after surgery. We developed a fully automated, segmentation-free deep learning model to support post-treatment response assessment using routine T2-weighted MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A longitudinal three-dimensional (3D) siamese convolutional neural network was trained using paired pre- and post-nCRT axial T2-weighted MRI volumes and a normalized signed voxel-wise difference map. The multitask framework simultaneously predicted rectal wall response (good response: modified Ryan score 0\u20131 vs poor response\u2009\u2265\u20092) and nodal status (ypN0 vs ypN\u2009+), from which pCR probability (ypT0N0) was derived. A retrospective single-center cohort of 195 patients was divided into training and independent test sets stratified by pCR status. Performance was evaluated using AUC-ROC and standard classification metrics with bootstrap-derived 95% confidence intervals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the independent test set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200949; pCR prevalence 18.5%), the model achieved an AUC-ROC of 0.71 (95% CI: 0.55\u20130.85) for pCR prediction. At the selected operating threshold, sensitivity was 100% (95% CI: 70.1\u2013100) and negative predictive value (NPV) was 100% (95% CI: 81.6\u2013100), with a specificity of 42.5% (95% CI: 28.5\u201357.8). The high NPV reflects the low prevalence of pCR in the study cohort and may vary across external populations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This fully automated longitudinal deep learning model demonstrated moderate discrimination and a high-sensitivity profile for pCR detection. Its performance suggests potential utility as a screening or triage tool to support multidisciplinary assessment, rather than to directly guide organ-preserving strategies. External multicenter validation is required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00384-026-05155-1","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T09:04:39Z","timestamp":1779440679000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated deep learning model for predicting pathological complete response in rectal cancer: A tool to organ-preserving strategies"],"prefix":"10.1007","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5337-9577","authenticated-orcid":false,"given":"Martin-Arevalo","family":"J.","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lopez-Mozos","family":"F.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moro-Valdezate","family":"D.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Perez-Santiago","family":"L.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Palomo-Lopez","family":"I.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Garcia-Botello S.","family":"A.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarazona-Llavero","family":"N.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cabrera-Perez","family":"B.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riera-Cardona","family":"M.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lillo-Albert","family":"G.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Millan","family":"M","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pla-Marti","family":"V.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"5155_CR1","doi-asserted-by":"publisher","unstructured":"Jia LL, Zheng QY, Tian JH, He DL, Zhao JX, Zhao LP, Huang G (2022) Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis. 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