AI-derived CT body composition phenotypes improve nutritional risk stratification in NSCLC

An integrated model combining AI-derived CT body composition, nutritional, and inflammatory markers predicted 90-day nutrition-related adverse events in NSCLC with AUCs of 0.842 and 0.816 in development and temporal validation cohorts, outperforming clinical-only models.

• Incidence of short-term nutrition-related adverse clinical trajectories was 34.3% in the development cohort and 32.6% in the validation cohort.
• Independent predictors: percentage weight loss, prognostic nutritional index, systemic immune-inflammation index, skeletal muscle index, and intermuscular adipose tissue volume.
• Higher risk scores were associated with worse overall survival and progression-free survival.

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