Review Article Advances in pre-treatment evaluation of pancreatic ductal adenocarcinoma: a narrative review Michele Fiore, Alessandro Coppola, Gian Marco Petrianni, Pasquale Trecca, Gabriele D’Ercole, Paola Cimini, Edy Ippolito, Damiano Caputo, Bruno Beomonte Zobel, Roberto Coppola, Sara Ramella

Background: Pancreatic cancer, with its poor prognosis, is frequently diagnosed in elderly patients who may be ineligible for surgery or chemoradiation due to age or comorbidities. Stereotactic body radiotherapy (SBRT) offers a targeted approach by delivering precise, high-dose radiation to a limited volume in few fractions. This study aims to evaluate the efficacy and safety of SBRT in elderly pancreatic cancer patients. Methods: Patients with pancreatic cancer received SBRT using Volumetric Modulated Arc Therapy in schedules of five dose fractions tailored to the tolerance of adjacent tissues. Progression-free survival (PFS), local progression-free survival (LPFS), metastasis-free survival (MFS) and overall survival (OS) were analysed. Toxicities were graded according to the Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Results: Median follow-up was 9.5 months (range, 4–37.2 months). The 1-year, 2-year and 3-year LPFS rates were 88%, 73% and 54%, respectively (median not reached). Median PFS, MFS and OS were 29.6 months, 32.6 months, and 24.1 months, respectively. SBRT was well tolerated with acute and late toxicity rates of 6% and 9%, respectively. Conclusionsd: SBRT appears to be an effective and safe option for elderly pancreatic cancer patients, achieving high local control with minimal toxicity.

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.