ArticleFrontiers in nutrition2026
Integrated CT-based assessment of muscle and adiposity for risk stratification in advanced pancreatobiliary cancer.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Pancreatobiliary cancers are highly aggressive, with limited prognostic markers in advanced stages. Cancer-associated malnutrition results in sarcopenia and adipose tissue depletion; however, the prognostic implications of comprehensive body composition metrics are unclear. Methods: We retrospectively analyzed 1,066 metastatic or recurrent pancreatobiliary cancer patients diagnosed between 2006 and 2017. Baseline clinical, laboratory, and nutritional data were collected. Skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI), visceral adipose tissue index (VATI), and muscle attenuation were assessed from computed tomography (CT) images at the third lumbar vertebra (L3). Optimal cut-off values were determined using maximally selected rank statistics. Survival was estimated using the Kaplan-Meier method and compared with the log-rank test. Cox proportional hazards models were used for multivariate analyses. Results: Sarcopenia was identified in 43% of patients and was associated with older age, male, diabetes mellitus, poor performance status, and unfavorable NRS-2002 score. CT-measured four body composition parameters (SMI, SATI, VATI, muscle attenuation) showed significant prognostic value for overall survival (OS), respectively, with excellent interobserver agreement (ICC range, 0.969-0.995). A composite risk score integrating all the four parameters showed superior prognostic discrimination, with median OS of 11.7, 7.8, and 4.9 months in the low-, moderate-, and high-risk groups ( Conclusion: Comprehensive CT-based body composition assessment, incorporating sarcopenia, adiposity, and muscle quality indices, improves prognostic stratification in advanced pancreatobiliary cancers. Integrating these body composition metrics with systemic inflammatory markers into routine evaluations may enhance individualized risk stratification and guide treatment decisions.
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