{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T12:33:28Z","timestamp":1788179608795,"version":"build-2803163510"},"reference-count":31,"publisher":"Wiley","issue":"7-8","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["ANZ Journal of Surgery"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and potential organ preservation. Of particular interest are predictors of pathological complete response (pCR) that can guide personalized treatment. There are currently no clinical biomarkers which can accurately predict neoadjuvant therapy (NAT) response but body composition (BC) measures present as an emerging contender. The primary aim of the study was to determine if artificial intelligence (AI) derived body composition variables can predict pCR in patients with LARC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>LARC patients who underwent NAT followed by surgery from 2012 to 2023 were identified from the Australian Comprehensive Cancer Outcomes and Research Database registry (ACCORD). A validated in\u2010house pre\u2010trained 3D AI model was used to measure body composition via computed tomography images of the entire Lumbar\u20103 vertebral level to produce a volumetric measurement of visceral fat (VF), subcutaneous fat (SCF) and skeletal muscle (SM). Multivariate analysis between patient body composition and histological outcomes was performed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 214 LARC patients treated with NAT, 22.4% of patients achieved pCR. SM volume (<jats:italic>P<\/jats:italic>\u2009=\u20090.015) and age (<jats:italic>P<\/jats:italic>\u2009=\u20090.03) were positively associated with pCR in both male and female patients. SCF volume was associated with decreased likelihood of pCR (<jats:italic>P<\/jats:italic>\u2009=\u20090.059).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This is the first study in the literature utilizing AI\u2010measured 3D Body composition in LARC patients to assess their impact on pathological response. SM volume and age were positive predictors of pCR disease in both male and female patients following NAT for LARC. Future studies investigating the impact of body composition on clinical outcomes and patients on other neoadjuvant regimens such as TNT are potential avenues for further research.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ans.18929","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T10:56:07Z","timestamp":1709895367000},"page":"1286-1291","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Artificial intelligence measured <scp>3D<\/scp> body composition to predict pathological response in rectal cancer patients"],"prefix":"10.1111","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5429-4740","authenticated-orcid":false,"given":"Matthew Y","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Surgery Western Precinct, University of Melbourne  Melbourne Victoria Australia"},{"name":"Department of Colorectal Surgery Western Health  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Surgery Western Precinct, University of Melbourne  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Hong","sequence":"additional","affiliation":[{"name":"Gibbs Lab Walter and Eliza Hall Institute  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josephine","family":"Yeung","sequence":"additional","affiliation":[{"name":"Department of Surgery Western Precinct, University of Melbourne  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Margaret","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology Western Health  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Gibbs","sequence":"additional","affiliation":[{"name":"Gibbs Lab Walter and Eliza Hall Institute  Melbourne Victoria Australia"},{"name":"Department of Medical Oncology Western Health  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian G.","family":"Faragher","sequence":"additional","affiliation":[{"name":"Department of Colorectal Surgery Western Health  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul N.","family":"Baird","sequence":"additional","affiliation":[{"name":"Department of Surgery Western Precinct, University of Melbourne  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin M.","family":"Yeung","sequence":"additional","affiliation":[{"name":"Department of Surgery Western Precinct, University of Melbourne  Melbourne Victoria Australia"},{"name":"Department of Colorectal Surgery Western Health  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/ans.18303"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/DCR.0000000000002451"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-021-02951-x"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.22.27749"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-023-05227-7"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/1047314"},{"key":"e_1_2_9_8_1","unstructured":"KoenkerR editorConfidence intervals for regression quantiles. 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