{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:59:37Z","timestamp":1790657977380,"version":"4.1.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["133561"],"award-info":[{"award-number":["133561"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Older patients with lung cancer receiving thoracic radiotherapy have heterogeneous outcomes that are not fully captured by conventional clinical descriptors. We evaluated whether artificial intelligence (AI)-assisted three-dimensional (3D) thoracic body-composition analysis of pretreatment radiotherapy simulation computed tomography (CT) could identify candidate body-composition imaging markers associated with overall survival.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This exploratory retrospective single-institution study included 88 patients aged 70 years or older who received thoracic radiotherapy for lung cancer between 2019 and 2021. Thoracic skeletal muscle and intermuscular fat were segmented using an AI-assisted workflow followed by expert review and Hounsfield unit-constrained refinement. Twelve knowledge-informed candidate groups were evaluated using conventional Cox regression. After marker selection, the selected marker pair was assessed in continuous form using bootstrap optimism correction, repeated five-fold cross-validation, and Uno\u2019s C-index. Penalized and alternative model-family analysis were performed as exploratory sensitivity analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>During follow-up, 28 deaths occurred. In an 18-case internal technical validation subset, the raw AI contours achieved Dice similarity coefficients of 0.930 for thoracic skeletal muscle and 0.723 for intermuscular fat. For Group 12, intermuscular adiposity index (IAI) and attenuation-weighted thoracic muscle fraction (TMF) were the variables that met the univariable screening criterion; the resulting two-marker model met the discrimination threshold without problematic collinearity. When modeled together, IAI values above the cohort median (hazard ratio [HR], 2.78; 95% confidence interval [CI], 1.25\u20136.18; P\u00a0=\u00a00.012) and TMF values above the cohort median (HR, 3.77; 95% CI, 1.52\u20139.32; P\u00a0=\u00a00.004) were associated with poorer overall survival. In continuous form, the selected marker pair had an optimism-corrected Harrell\u2019s C-index of 0.751 (95% CI, 0.661\u20130.849), a mean repeated cross-validation C-index of 0.753 \u00b1 0.112, and Uno\u2019s C-index of 0.737. These performance estimates were conditional on prior marker selection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI-assisted analysis of routine radiotherapy simulation CT identified IAI and TMF as candidate body-composition imaging markers associated with overall survival in this clinically heterogeneous cohort. The findings are exploratory and require independent evaluation in larger, clinically characterized cohorts before clinical use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1946212","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:41:31Z","timestamp":1790656891000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-assisted 3D thoracic body composition analysis identifies candidate imaging markers associated with overall survival in older patients with lung cancer receiving radiotherapy"],"prefix":"10.3389","volume":"16","author":[{"given":"Saerom","family":"Sung","sequence":"first","affiliation":[{"name":"Department of Radiation Convergence Engineering, Yonsei University","place":["Wonju, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun 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