{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:44:29Z","timestamp":1787067869366,"version":"build-2736575974"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Background: With rectum-sparing protocols becoming more common for rectal cancer treatment, this study aimed to predict the pathological complete response (pCR) to preoperative chemoradiotherapy (pCRT) in rectal cancer patients using pre-treatment MRI and a radiomics-based machine learning approach. Methods: We divided MRI-data from 102 patients into a training cohort (n = 72) and a validation cohort (n = 30). In the training cohort, 52 patients were classified as non-responders and 20 as pCR based on histological results from total mesorectal excision. Results: We trained various machine learning models using radiomic features to capture disease heterogeneity between responders and non-responders. The best-performing model achieved a receiver operating characteristic area under the curve (ROC-AUC) of 73% and an accuracy of 70%, with a sensitivity of 78% and a positive predictive value (PPV) of 80%. In the validation cohort, the model showed a sensitivity of 81%, specificity of 75%, and accuracy of 80%. Conclusions: These results highlight the potential of radiomics and machine learning in predicting treatment response and support the integration of advanced imaging and computational methods for personalized rectal cancer management.<\/jats:p>","DOI":"10.3390\/life14121530","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T04:21:36Z","timestamp":1732249296000},"page":"1530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal Cancer"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6822-1430","authenticated-orcid":false,"given":"Filippo","family":"Crim\u00ec","sequence":"first","affiliation":[{"name":"Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6721-3946","authenticated-orcid":false,"given":"Carlo","family":"D\u2019Alessandro","sequence":"additional","affiliation":[{"name":"Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Zanon","sequence":"additional","affiliation":[{"name":"Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1881-0605","authenticated-orcid":false,"given":"Francesco","family":"Celotto","sequence":"additional","affiliation":[{"name":"Third Surgical Clinic, Department of Surgical, Oncological and Gastroenterological Sciences (DiSCOG), University of Padova, 35128 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9312-4675","authenticated-orcid":false,"given":"Christian","family":"Salvatore","sequence":"additional","affiliation":[{"name":"DeepTrace Technologies S.R.L., Via Conservatorio 17, 20122 Milano, Italy"},{"name":"Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3066-4276","authenticated-orcid":false,"given":"Matteo","family":"Interlenghi","sequence":"additional","affiliation":[{"name":"DeepTrace Technologies S.R.L., Via Conservatorio 17, 20122 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7191-5417","authenticated-orcid":false,"given":"Isabella","family":"Castiglioni","sequence":"additional","affiliation":[{"name":"Dipartimento di Fisica Giuseppe Occhialini, Universit\u00e0 degli Studi di Milano Bicocca, Piazza della Scienza 3, 20126 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2020-9365","authenticated-orcid":false,"given":"Emilio","family":"Quaia","sequence":"additional","affiliation":[{"name":"Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5289-9925","authenticated-orcid":false,"given":"Salvatore","family":"Pucciarelli","sequence":"additional","affiliation":[{"name":"Third Surgical Clinic, Department of Surgical, Oncological and Gastroenterological Sciences (DiSCOG), University of Padova, 35128 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7275-2573","authenticated-orcid":false,"given":"Gaya","family":"Spolverato","sequence":"additional","affiliation":[{"name":"Third Surgical Clinic, Department of Surgical, Oncological and Gastroenterological Sciences (DiSCOG), University of Padova, 35128 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.3748\/wjg.v30.i3.211","article-title":"Why is early detection of colon cancer still not possible in 2023?","volume":"30","author":"Tonini","year":"2024","journal-title":"World J. 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