Machine learning models integrating 2D and 3D intratumoral heterogeneity features from multi-sequence MRI: accurate prediction of perineural invasion in pancreatic ductal adenocarcinoma

Introduction Perineural invasion (PNI) occurs in the majority of pancreatic ductal adenocarcinoma (PDAC) cases and is strongly associated with local recurrence, neuropathic pain, and poor survival, yet no reliable method exists for its preoperative identification. Here, we developed and externally validated a multi-sequence MRI-based intratumoral heterogeneity (ITH) scoring framework integrated with machine learning for the non-invasive prediction of PNI in PDAC. Methods Using preoperative T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MRI from 205 surgically resected patients across four medical centers (100 training, 43 internal test, and 28 and 34 external validation patients), we extracted two-dimensional and three-dimensional ITH features via K-means clustering-based subregion partitioning and incorporated independent clinical predictors tumor location and clinical T-stage to construct combined predictive models. Eleven machine learning algorithms were systematically evaluated. Results The combined clinical-ITH model achieved an area under the curve (AUC) of 0.907 in the internal test set and AUCs of 0.860 and 0.869 in the two external validation cohorts. Corresponding AUCs were 0.881, 0.860, and 0.850 for the ITH model and 0.758, 0.342, and 0.700 for the clinical logistic regression model. Discussion SHAP analysis ranked three T2WI-derived features as the largest contributors to the combined model output, feature effects varied in direction and were not uniformly monotonic. These findings demonstrate that multi-sequence MRI-based ITH quantification provides an interpretable approach to preoperative PNI assessment with consistent discrimination across the two external surgical cohorts, offering a potential tool to guide surgical planning and adjuvant therapy selection in pancreatic cancer.