Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients
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Le résumé fourni par la source
BACKGROUND: 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. METHODS: 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-house pre-trained 3D AI model was used to measure body composition via computed tomography images of the entire Lumbar-3 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. RESULTS: Of 214 LARC patients treated with NAT, 22.4% of patients achieved pCR. SM volume (P = 0.015) and age (P = 0.03) were positively associated with pCR in both male and female patients. SCF volume was associated with decreased likelihood of pCR (P = 0.059). CONCLUSION: This is the first study in the literature utilizing AI-measured 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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé, mais le titre doit être comparé manuellement.
- Titre Crossref
- Artificial intelligence measured <scp>3D</scp> body composition to predict pathological response in rectal cancer patients
- Date Crossref
- 08/03/2024
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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The University of Melbourne Department of Surgery pays non établi dans la noticeUniversité ou école supérieure
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Western Health pays non établi dans la noticeÉtablissement de santé
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Walter and Eliza Hall Institute of Medical Research pays non établi dans la noticeOrganisation à but non lucratif
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University of Melbourne Melbourne Victoria Australia Department of Surgery Western Precinct pays non établi dans la noticeUniversité ou école supérieure
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Department of Colorectal Surgery pays non établi dans la noticeInstitution
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Gibbs Lab Walter and Eliza Hall Institute Melbourne Victoria Australia pays non établi dans la noticeStructure de recherche
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Department of Medical Oncology pays non établi dans la noticeInstitution
Department of Surgery — The University of Melbourne, Western Health et Walter and Eliza Hall Institute of Medical Research, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.