{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:02:19Z","timestamp":1779894139978,"version":"3.53.1"},"reference-count":47,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Reports"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Patients with locally advanced rectal cancer show variable responses to neoadjuvant chemoradiotherapy (NACTRT), making early prediction of treatment response clinically important. Radiomics and machine learning may offer a potential non\u2010invasive imaging biomarker to identify patients who are more likely to respond to treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>We investigated a machine learning\u2010based radiomic approach to predict tumor response (TR) to NACTRT in patients with adenocarcinoma of the rectum from baseline and post\u2010NACTRT magnetic resonance imaging (MRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>One hundred patients with locally advanced rectal cancer, who underwent pre\u2010 and post\u2010treatment MRI after NACTRT were included in the study after approval from the institutional ethics committee. One hundred twenty\u2010four radiomic features were extracted using the TexRad softwareTm, from pre\u2010 and post\u2010treatment T2W MRI, which included first\u2010order, GLCM, and shape features. Features were selected by the recursive feature elimination (RFE) algorithm. Five machine learning classifiers\u2014support vector machine (SVM), random forest (RF), gradient boost (GB), Naive Bayes (NB) and AdaBoost (AB) classifiers were used to develop a prediction model for predicting TR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Five radiomic features were selected using for TR using RFE using RF algorithm. The RF model with 10\u2010fold internal cross\u2010validation performed the best for predicting TR. The predictive performance of the RF model for TR was found to be AUC: 0.79\u2009\u00b1\u20090.15, accuracy: 0.72\u2009\u00b1\u20090.12, and precision: 0.77\u2009\u00b1\u20090.10 for the test cohort respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>RF classifier based radiomic model provided a non\u2010invasive method to predict TR in patients of carcinoma rectum undergoing NACTRT treatment. The predictive potential of the radiomic features further requires validation in a larger cohort of patients in a multicentric study for clinical translation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cnr2.70574","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T04:44:10Z","timestamp":1779252250000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>MRI<\/scp>\n                    Based Radiomics as an Imaging Biomarker for Locally Advanced Carcinoma Rectum: Predicting Tumor Response Following Neoadjuvant Chemoradiotherapy as an Organ Preservation Strategy"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2047-0037","authenticated-orcid":false,"given":"Abhinav","family":"Puppalwar","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Tata Memorial Centre  Mumbai India"},{"name":"Homi Bhaba National 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