Background/Objectives: Early progression (EP) occurs in a subset of patients with locally advanced pancreatic cancer (LAPC), limiting the clinical benefit of treatment, and it remains difficult to predict. Methods: We developed a multiparametric predictive model integrating baseline 18F-FDG PET/CT radiomic features with clinical and biological data. A total of 242 radiomic features were extracted from each imaging modality (CT and PET), including first-order, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP-TOP) features, and combined with PET-derived metrics and clinical variables. Model development included cross-validation procedures and rigorous feature selection, followed by the training of a two-level decision tree classifier. Results: The model achieved an accuracy of 80.7% and an area under the curve (AUC) of 0.83. Integrated analysis of CT and PET texture enabled the identification of patients at high risk of EP prior to treatment initiation. Conclusions: PET/CT-based radiomic biomarkers, combined with clinical data, can non-invasively capture tumor heterogeneity and improve risk stratification in LAPC, supporting more personalized therapeutic decision-making.
Fiore et al. (Thu,) studied this question.