A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole-Slide Pathology Images.

Delayed diagnosis and treatment resistance make pancreatic ductal adenocarcinoma (PDAC) mortality rates high. Identifying molecular subtypes can improve treatment, but current methods are costly and time-consuming. In this study, deep learning models were utilized to identify histological features that classify PDAC molecular subtypes based on routine hematoxylin-eosin (H&E)-stained histopathological slides. 97 histopathology slides associated with resectable PDAC from the Cancer Genome Atlas (TCGA) project were utilized to train a deep learning model and tested the performance on 44 needle biopsy material (110 slides) from a local annotated patient cohort. The model achieved balanced accuracy of 96.19% and 83.03% in identifying the classical and basal subtypes of PDAC in the TCGA and the local cohort, respectively. This study provides a promising method to cost-effectively and rapidly classifying PDAC molecular subtypes based on routine H&E slides, potentially leading to more effective clinical management of this disease.