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Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.

Purpose: To assess interobserver variability and accuracy of preoperative computed tomography (CT) and magnetic resonance imaging (MRI) in pancreatic ductal adenocarcinoma (PDAC) size estimation using surgical specimens as standard of reference. Methods: Patients with PDAC who underwent preoperative CT and MRI examinations before surgery were included. PDAC largest axial dimension was measured by 2 readers on 8 MRI sequence and 2 CT imaging phases (pancreatic parenchymal and portal venous). Measurements were compared to actual tumour size at pathologic examination. Interobserver variability was assessed using intraclass correlation coefficients (ICC) and Bland-Altman plots. Differences in tumour size (Δdiameter) between imaging and actual tumour size were searched using Wilcoxon rank sum test. Results: Twenty-nine patients (16 men; median age, 70 years) with surgically resected PDAC were included. Interobserver reproducibility was good to excellent for all MRI sequences and the 2 CT imaging phases with ICCs between .862 (95%CI: .692-.942) for fat-saturated in-phase T1-weighted sequence and .955 (95%CI: .898-.980) for portal venous phase CT images. Best accuracy in PDAC size measurement was obtained with pancreatic parenchymal phase CT images with median Δdiameters of −2 mm for both readers, mean relative differences of −9% and −6% and no significant differences with dimensions at histopathological analysis ( P = .051). All MRI sequences led to significant underestimation of PDAC size (median Δdiameters, −6 to −1 mm; mean relative differences, −21% to −11%). Conclusions: Most accurate measurement of PDAC size is obtained with CT images obtained during the pancreatic parenchymal phase. MRI results in significant underestimation of PDAC size.