Author links open overlay panel, , , , , , , , , , Highlights•Fully-automated AI-derived body composition variables offer an oppportunistic adjunct to improve patient treatment planning.
•Fully automated AI-derived CT body composition variables of myosteatosis and increased visceral fat density add value to clinicopathological biomarkers in predicting OS in all subgroups of patients with PDAC.
•Myosteatosis and visceral fat density are predictive imaging biomarkers for overall survival in patients with PDAC, potentially enabling upfront risk stratification.
AbstractRationale and objectivesUtilizing a fully automated AI-generated body composition analysis (BCA) from PDAC staging computed tomography (CT) imaging to discover predictive imaging biomarkers for overall survival (OS).
Material and methodsRoutine PDAC staging CTs (07/2012–12/2020) and clinicopathological data (Eastern Cooperative Oncology Group (ECOG) performance status, resection status, chemotherapy, age, CA19–9, Charlson Comorbidity Index, BMI) from four tertiary centers were collected retrospectively. Using a 3:1 split (training:holdout), we fitted Cox regression OS using every possible combination of 7 clinicopathological and 9 BCA variables: skeletal muscle index (SMI), area and density of total muscle compartment (TMC), skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and selected the combination with the lowest information complexity (ICOMP). The added value of BCA was calculated by comparing the BCA model with the base model (without BCA variables).
ResultsAnalysis included 472 PDAC patients (213 female, mean age 67.9 ± 11.5 years, resectable n = 170, unresectable n = 106, metastatic n = 196). Four clinicopathological (ECOG, resection status, chemotherapy, CA19–9) and 5 BCA variables (SMI, SM density, VAT density, TMC area, VAT area) were selected. Decreased SM density (myosteatosis) and increased VAT density showed strong association with OS (p = 0.0094 and 0.0019, respectively). The BCA model showed superior performance compared to the base model in all subgroups (AUC: resectable 0.76 versus 0.70, unresectable 0.76 versus 0.69, and metastatic 0.80 versus 0.75).
ConclusionBCA-identified myosteatosis and increased VAT density to be predictive imaging biomarkers for OS in all PDAC subgroups, potentially adding value to upfront risk stratification.
KeywordsBody composition
Pancreatic carcinoma
Computed tomography
© 2025 The Authors. Published by Elsevier Ltd.
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