Association Between Computed Tomography-Based AI-Derived Body Composition and Survival in Patients With Pancreatic Ductal Adenocarcinoma.

Abstract

A deeper understanding of the factors influencing survival in patients with pancreatic ductal adenocarcinoma (PDAC) is essential for optimizing treatment strategies. This study investigates the independent association of body composition parameters with overall survival in patients with PDAC. This retrospective multisite cohort study included patients diagnosed with PDAC. Diagnostic computed tomography scans were retrieved, and body composition was evaluated using a validated deep learning-based segmentation algorithm that measured tissue volume and density in a 20 cm vertical abdominal section. A total of 1,666 patients with PDAC were included, 938 male (56.3%) and median age 69 years (interquartile range 61-76). In the subgroup of patients who underwent surgical resection (n = 509), myosteatosis (intramuscular infiltration of adipose tissue; hazard ratio [HR] 1.56, 95% confidence interval [CI] 1.16-2.11, P = 0.004), sarcopenic obesity (HR 1.75, 95% CI 1.06-2.91, P = 0.03), and less subcutaneous adipose tissue (HR 1.09, 95% CI 1.03-1.16, P = 0.002) were associated with higher mortality. In patients receiving palliative systemic therapy (n = 439), lower skeletal muscle density (HR 1.43, 95% CI 1.03-1.99, P = 0.03) was associated with higher mortality. In patients who did not undergo tumor-targeted treatment (n = 718), less visceral adipose tissue (HR 1.04, 95% CI 1.01-1.08, P = 0.02) was associated with higher mortality. Body composition parameters, derived from computed tomography scans at the time of PDAC diagnosis, particularly low skeletal muscle density, sarcopenic obesity, and low adipose tissue, are independently associated with overall survival in patients with PDAC. Evaluating body composition at diagnosis could enhance clinical decision-making and enable more personalized treatment strategies.