The Role of Computed Tomography-Determined Total Tumor Volume at Baseline in Predicting Outcomes of Patients with Locally Advanced Unresectable or Metastatic Pancreatic Ductal Adenocarcinoma

<b>Background:</b> Total tumor volume (TTV), derived from imaging data, has emerged as a potential prognostic biomarker in various cancers. This study aimed to evaluate the impact of TTV on outcomes in advanced pancreatic ductal adenocarcinoma (PDAC) and to validate a survival prediction model combining TTV with baseline clinico-biological markers. <b>Materials and Methods:</b> We conducted a retrospective analysis of 150 patients with locally advanced or metastatic PDAC treated with first-line FOLFIRINOX from 2010 to 2021. TTV was calculated by manually segmenting all visible lesions on baseline CT scans. Progression-free survival (PFS) and overall survival (OS) were the primary endpoints. A cut-off value for TTV predicting 6-month PFS was determined in 140 patients using AUC and Youden's Index and then applied to OS analysis. A multivariate Cox regression model incorporating TTV, CA 19-9, and neutrophil-to-lymphocyte ratio (NLR) was developed in 94 patients to establish a survival risk score. <b>Results:</b> 12,028 lesions were annotated. OS was slightly but significantly different between TTV above and below the median value of 69.60 cm³ (12.4 vs. 13.5 months, <i>p</i> = 0.0269). A cut-off of 400 cm³ distinguished two groups: patients with TTV > 400 cm³ had significantly shorter OS (9.4 months) compared to those with TTV ≤ 400 cm³ (13.0 months, <i>p</i> = 0.0056). A similar trend was observed for PFS, though not statistically significant (7.4 months for TTV > 400 cm³ vs. 8.2 months for TTV ≤ 400 cm³, <i>p</i> = 0.0735). The combined model achieved a mean c-index of 0.62 for PFS and 0.64 for OS. Based on the risk score, high-risk patients had significantly worse median PFS (5.5 vs. 9.2 months, <i>p</i> = 0.0008) and median OS (7.2 vs. 13.5 months, <i>p</i> < 0.0001). <b>Conclusions:</b> TTV is a valuable prognostic marker in advanced PDAC. A model integrating TTV with biological markers enhances survival prediction and supports risk stratification in clinical practice.