Computed tomography quantitative imaging features for pancreatic ductal adenocarcinoma after neoadjuvant therapy: a narrative review.

Background and Objective: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal solid malignancies. Neoadjuvant therapy (NAT) has been routinely used in borderline resectable and locally advanced cases, now it is also gradually expanding to some resectable cases. Post-NAT assessment on computed tomography (CT) is intrinsically challenging, as therapy-induced stromal remodeling, fibrosis, and inflammation may obscure viable tumor, while size-based criteria correlate poorly with pathological response and survival. This narrative review aims to synthesize CT-based quantitative imaging features for PDAC after NAT and to clarify how these imaging biomarkers may support clinically relevant multidisciplinary decision-making. Methods: A narrative review was conducted using a three-layer literature identification strategy. A primary search of PubMed and Web of Science Core Collection was performed on 7 April 2026 to identify English-language articles published from 2013 to 2026. The search focused on PDAC, NAT, CT, and CT-derived quantitative approaches, including radiomics, perfusion CT, dual-energy/spectral CT, and photon-counting CT (PCCT), together with clinically relevant endpoints such as response, resectability, margin status, survival, recurrence, and prognosis. Targeted supplementary retrieval and manual anchor retrieval were additionally used for key reviews, foundational pathology and tumor microenvironment (TME) references, complementary magnetic resonance imaging (MRI) and positron emission tomography (PET) literature, and methodological framework papers. Key Content and Findings: Quantitative CT features after NAT can be organized around four multidisciplinary team (MDT) decisions: assessment of tumor-vessel interface resectability and the probability of margin-negative (R0) resection, NAT stewardship, surgical-window timing, and early recurrence risk stratification. The most informative measurement layers include interpretable morphologic and enhancement-based metrics, longitudinal delta features, perfusion-derived functional parameters, iodine- and material-sensitive metrics from energy-resolved CT, and multi-compartment radiomics or habitat analysis. Conclusions: Building on this evidence, we outline a pragmatic, CT-centric measurement ladder that progresses from interpretable enhancement and iodine metrics to interface focused features and habitat-level heterogeneity, aiming to reduce inter-reader variability and improve multicenter reproducibility, with MRI and PET positioned as complementary modalities for future multimodal validation.