{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:39:52Z","timestamp":1783021192688,"version":"3.54.6"},"reference-count":41,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"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":["ANZ Journal of Surgery"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) is a key prognostic marker with implications for response\u2010adapted management. Although magnetic resonance imaging (MRI) is central to response assessment, differentiating residual tumour from treatment\u2010related changes remains challenging. Artificial intelligence (AI) and machine learning (ML) models applied to MRI show promise in predicting pCR; however, variability in methodology and performance limits clinical translation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A search of Embase, Medline, Cochrane and Web of Science was conducted in April 2025 in accordance with Preferred Reporting Items for Reviews and Meta\u2010Analysis (PRISMA) guidelines. Eligible studies used MRI\u2010only AI or ML models to predict pCR following chemoradiotherapy in adults with rectal cancer. Screening, full\u2010text review and data extraction were performed independently by two reviewers. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS\u20102).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Twenty\u2010two studies comprising 94 predictive models were included. Most studies were retrospective, used T2\u2010weighted MRI and demonstrated variability in MRI protocols, modelling methods and validation strategies. Only five studies conducted external validation. Median AUC was 0.801, with performance ranging from poor to excellent (AUC 0.49\u20130.997).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>MRI\u2010based AI models demonstrate moderate discriminative performance for predicting pCR following neoadjuvant therapy in LARC. However, methodological heterogeneity, inconsistent reporting and limited external validation currently hinder generalisability. Greater methodological standardisation and multicentre external validation are required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ans.70724","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:21:51Z","timestamp":1778048511000},"page":"1471-1480","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Models Using Magnetic Resonance Imaging to Predict Response to Chemoradiotherapy in Rectal Cancer: A Systematic Review"],"prefix":"10.1111","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7441-2126","authenticated-orcid":false,"given":"Winnie","family":"Lay","sequence":"first","affiliation":[{"name":"Western Health  Melbourne Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8622-2701","authenticated-orcid":false,"given":"Ha My 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