A clinician-centric intelligent method towards reliable pancreatic cancer vascular invasion assessment: a retrospective, multi-centre study

SummaryBackground Pancreatic cancer is highly aggressive, with high post-resection recurrence and poor survival. Accurate preoperative assessment of vascular invasion is essential but remains clinically unmet. This study aimed to enable reliable automated assessment of vascular invasion using routine computed tomography (CT). Methods We developed CRVIA, a clinician-centric artificial intelligence (AI) method that encodes clinical expertise into invasion-omics and emulates clinician's decision-making through causality-enhanced modelling. The model was trained on 1251 cases and validated on internal (1104 cases) and external (3575 cases from five centres) cohorts. Reader studies involving eight radiologists were conducted to evaluate the efficacy of CRVIA's assistance. Primary outcome measures included classification performance, feature distance, and inter-reader agreement. Statistical analyses were conducted using paired t-tests, Mann–Whitney U, Pearson's chi-squared, and permutation tests. Findings The study cohort comprised 5930 cases from 2062 patients (median age 63.0 years IQR 56.0–70.0; 41.1% female). CRVIA achieved AUCs of 0.946 (95% CI 0.929–0.962) internally and 0.943 (95% CI 0.932–0.952) externally, outperforming nine comparison approaches spanning radiomics to foundation models. Performance remained stable across stages, vessel types, and segmentation variations. In reader studies, CRVIA exceeded senior radiologists' accuracy, elevated junior radiologists to expert-level performance, and substantially improved inter-reader agreement. Interpretation This study presents a reliable, open-source AI tool (code: https://github.com/SJTUBME-QianLab/CRVIA) for accurate, stable, and interpretable vascular invasion assessment in pancreatic cancer, which potentially supports precise treatment, encourages collaborative research, and ultimately benefits patients, especially for resource-limited regions. Funding National Natural Science Foundation of China and Shanghai Natural Science Foundation.