Development of a deep learning system for predicting biochemical recurrence in prostate cancer

Abstract

Background

Biochemical recurrence (BCR) occurs in 20%–40% of men with prostate cancer (PCa) who undergo radical prostatectomy. Predicting which patients will experience BCR in advance helps in formulating more targeted prostatectomy procedures. However, current preoperative recurrence prediction mainly relies on the use of the Gleason grading system, which omits within-grade morphological patterns and subtle histopathological features, leaving a significant amount of prognostic potential unexplored.

Methods

We collected and selected a total of 1585 prostate biopsy images with tumor regions from 317 patients (5 Whole Slide Images per patient) to develop a deep learning system for predicting BCR of PCa before prostatectomy. The Inception_v3 neural network was employed to train and test models developed from patch-level images. The multiple instance learning method was used to extract whole slide image-level features. Finally, patient-level artificial intelligence models were developed by integrating deep learning -generated pathology features with several machine learning algorithms.

Results

The BCR prediction system demonstrated great performance in the testing cohort (AUC = 0.911, 95% Confidence Interval: 0.840–0.982) and showed the potential to produce favorable clinical benefits according to Decision Curve Analyses. Increasing the number of WSIs for each patient improves the performance of the prediction system. Additionally, the study explores the correlation between deep learning -generated features and pathological findings, emphasizing the interpretative potential of artificial intelligence models in pathology.

Conclusions

Deep learning system can use biopsy samples to predict the risk of BCR in PCa, thereby formulating targeted treatment strategies.