Assessing Tumor Differentiation in Pancreatic Ductal Adenocarcinoma: A Comparative Study of Four Diffusion MRI Models

Purpose The differentiation grade of pancreatic ductal adenocarcinoma (PDAC) is a crucial determinant of its aggressiveness and patient prognosis. Recently, advanced diffusion models have been increasingly applied to assess the grading of many tumors. However, the value of these models in discriminating PDAC differentiation grades remains unclear. Methods A retrospective analysis was conducted on 71 patients with pathologically confirmed PDAC between July 2022 and March 2025. Clinicopathological data and conventional imaging features were collected. Eleven diffusion parameters were derived from four advanced diffusion models via multi-model diffusion imaging (DXI) technology: intravoxel incoherent motion (IVIM: f , D* , D ), diffusion kurtosis imaging (DKI: D , K ), fractional order calculus (FROC: D , β , μ ), and continuous-time random walk (CTRW: D , β , α ). Based on postoperative histological differentiation, patients were categorized into a low-grade group ( n = 38) and a high-grade group ( n = 33). The clinicopathological features, imaging characteristics, and diffusion parameters were compared between the two groups. Receiver operating characteristic (ROC) curves were utilized to evaluate the diagnostic performance of these indicators in predicting the differentiation grade of PDAC, and the DeLong test was employed to compare differences in the area under the curve (AUC). Parameter stability was assessed using bootstrap internal validation with 1000 resamples. Results The high-grade PDAC group exhibited significantly lower values in IVIM- f , DKI- D , and CTRW- α metrics (all p < 0.0045). Among all metrics, CTRW- α had the numerically highest AUC of 0.823. A CTRW- α cutoff of < 0.856 diagnosed high-grade PDAC with 81.8% sensitivity and 71.1% specificity. Lymph node status differed significantly between the low- and high-grade groups ( p = 0.024). Conclusions DXI technology provides valuable advanced diffusion models and derived parameters for non-invasively assessing PDAC differentiation grade, providing a novel tool for clinical assessment.