Predicting biochemical recurrence of prostate cancer with artificial intelligence.

ABSTRACT:

Background

The first sign of metastatic prostate cancer after radical prostatectomy is rising PSA levels in the blood, termed biochemical recurrence. The prediction of recurrence relies mainly on the morphological assessment of prostate cancer using the Gleason grading system. However, in this system, within-grade morphological patterns and subtle histopathological features are currently omitted, leaving a significant amount of prognostic potential unexplored.

Methods

To discover additional prognostic information using artificial intelligence, we trained a deep learning system to predict biochemical recurrence from tissue in H&E-stained microarray cores directly. We developed a morphological biomarker using convolutional neural networks leveraging a nested case-control study

SUBMITTER: Pinckaers H 

PROVIDER: S-EPMC9177591 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature