Bogdanski, Aleksander M

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. DISCUSSION: 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.

Objectives: New methods are needed to detect pancreatic ductal adenocarcinoma (PDAC) earlier to improve outcomes. We previously reported that a panel of protein N-glycosylation traits (NGTs) discriminated PDAC from healthy controls with an area under the curve (AUC) of 0.81–0.88. However, it remained unclear whether this panel accurately differentiates PDAC from other benign pancreatic disorders. Our study aims to evaluate the performance of the NGT panel in combination with CA19-9 in a diverse cohort, including PDAC cases, healthy controls, and controls with benign pancreatic disorders. Methods: Protein N-glycosylation profiles were determined in plasma samples using an in-house developed mass spectrometry assay. CA19-9 levels were measured using a routine immunoassay test. Results of total plasma NGTs and CA19-9 were evaluated separately as well as in combination. Logistic regression was performed to calculate odds ratios (ORs), AUC, sensitivity, and specificity to determine the performance of NGTs and CA19-9 in distinguishing PDAC from controls. Results: In total, 221 individuals were included: 45 (20.4%) with PDAC, and 176 (79.6%) controls (53 healthy and 123 with benign pancreatic disease). The AUC for differentiating PDAC from the total control cohort based on the combination of the NGT panel and CA19-9 was 0.94 (95% CI, 0.90–0.97), with a sensitivity of 0.89 (95% CI, 0.78–0.98) and specificity of 0.86 (95% CI, 0.81–0.91). Comparison of PDAC cases with healthy controls only resulted in an AUC of 0.96 (95% CI, 0.93–0.99), with a sensitivity of 0.84 (95% CI, 0.73–0.93) and specificity of 0.98 (95% CI, 0.94–1.00). Conclusions: Both plasma NGTs and CA19-9 distinguish PDAC from a diverse control cohort. The accuracy further improves when these readouts are combined, showing promise for future early detection methods.