Deep learning model for predicting extraprostatic extension of prostate cancer based on H&E-stained biopsy digital images

Abstract

Background: To develop and validate a deep learning pipeline using prostate biopsy H&E slides to predict extraprostatic extension (EPE) in prostate cancer (PCa) patients. Methods: A total of 2592 preoperative biopsy H&E slides from 260 consecutive PCa patients who underwent radical prostatectomy were collected from January 2019 to October 2023. Whole-slide images (WSIs) were digitized, tumor regions were annotated, and 224 × 224 pixel patches were extracted. The dataset was randomly divided into training and testing sets at the patient level in a ratio of 8:2. A tumor classification model and an EPE prediction model based on multiple instance learning were trained. Subsequently, we conducted an interpretability analysis of the EPE model and further carried out a correlation analysis between the predicted probabilities of the EPE and the biochemical recurrence (BCR) of the patients. Results: The ConvNeXt model achieved the best performance in tumor classification, with an accuracy of 0.965 and an area under the curve (AUC) of 0.981 on the test set. For EPE prediction, the model achieved AUCs of 0.943 and 0.886 in the training and test sets, respectively. Key features identified by the model, such as nuclear characteristics, were significantly associated with EPE. Predicted EPE probabilities were strongly correlated with BCR (p = 0.01). Conclusions: The AI pathology model accurately predicts postoperative EPE via biopsy slides, achieving an AUC of 0.886 on the test set, offering a novel, feasible PCa preoperative risk stratification method to aid personalized treatment.