{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:13:11Z","timestamp":1788448391348,"version":"build-2803163510"},"reference-count":42,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"the Digital Medical Products Development Based on Medical Data Synthesis and AI Technologies Program","award":["RS-2025-02305698"],"award-info":[{"award-number":["RS-2025-02305698"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Accurate identification of complete response (CR) after neoadjuvant chemoradiotherapy (NCRT) is essential for selecting candidates for watch-and-wait treatment in advanced rectal cancer. This study aimed to develop and evaluate a magnetic resonance imaging (MRI) radiomics-based machine learning model to classify CR and Non-CR in post-NCRT rectal MRI images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using region-of-interest masks, 107 radiomic features were extracted and normalized to a 0\u20131 range using min-max scaling. Four feature selection methods (ANOVA, RFE, SBS, and LASSO) were paired with four classifiers (LR, SVM, RF, and XGB), and all combinations were evaluated through 5-fold cross-validation in the training set using mean ROC AUC as the comparison metric.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 16 combinations, RFE + SVM achieved the highest cross-validation AUC of 0.89. On an independent test set, the model achieved a sensitivity of 0.84, specificity of 0.80, accuracy of 0.82, and F1-score of 0.82. The most informative features were intensity and local texture features, including TotalEnergy, 10Percentile, Coarseness, Strength, Correlation, ZoneEntropy, and Idmn.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>MRI radiomics combined with machine learning shows preliminary promise as an exploratory approach for classifying CR and Non-CR after NCRT in rectal cancer. Given the retrospective, single-center design and the absence of external validation, these findings should be interpreted as preliminary, and prospective multicenter validation is required before clinical implementation. A multicenter external validation integrating endoscopy and digital rectal examination findings is planned to enhance generalizability and clinical utility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261484806","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T16:19:47Z","timestamp":1788193187000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["MRI radiomics-based machine learning model for complete response classification after chemoradiotherapy in advanced rectal cancer"],"prefix":"10.1177","volume":"12","author":[{"given":"Jin Young","family":"Min","sequence":"first","affiliation":[{"name":"Gachon University Gil Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun Young","family":"Park","sequence":"additional","affiliation":[{"name":"Gachon University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Jae","family":"Kim","sequence":"additional","affiliation":[{"name":"Gachon University Gil Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youngbae","family":"Jeon","sequence":"additional","affiliation":[{"name":"Gachon University College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-6038","authenticated-orcid":false,"given":"Kwang Gi","family":"Kim","sequence":"additional","affiliation":[{"name":"Gachon University Gil Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeong-Heum","family":"Baek","sequence":"additional","affiliation":[{"name":"Gachon University College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.sla.0000141194.27992.32"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-015-4687-9"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-018-1627-8"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1097\/DCR.0000000000000667"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0b013e31828f6c91"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-019-01894-8"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2532090027"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-011-1607-5"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018172300"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5719-1"},{"key":"e_1_3_6_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/bjc.2017.320"},{"key":"e_1_3_6_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/bjs.10898"},{"key":"e_1_3_6_14_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169"},{"key":"e_1_3_6_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06565-2"},{"key":"e_1_3_6_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2012.06.010"},{"key":"e_1_3_6_17_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01474-3"},{"key":"e_1_3_6_18_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.211986"},{"key":"e_1_3_6_19_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.211986"},{"key":"e_1_3_6_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11050756"},{"key":"e_1_3_6_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2024.111839"},{"key":"e_1_3_6_22_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.541663"},{"key":"e_1_3_6_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)31078-X"},{"key":"e_1_3_6_24_2","doi-asserted-by":"publisher","DOI":"10.47162\/RJME.62.2.07"},{"key":"e_1_3_6_25_2","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"e_1_3_6_26_2","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa F","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, et al. 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