Independent validation of paige prostate: Assessing clinical benefit of an artificial intelligence tool within a digital diagnostic pathology laboratory workflow.

e14076 Background: The most common approach to diagnose prostate cancer is the “whole gland biopsy procedure,” in which numerous cores (≥12) are taken from different regions of the gland to maximize the chances of detecting small cancers; the presence of cancer in any of these cores is significant to the patient. If concerning features that are not fully diagnostic of cancer are identified, the pathologist may defer the final diagnosis until additional studies (e.g. immunohistochemistry) have been performed. We recently developed an artificial intelligence (AI)-based system for the assessment of cancer in prostate biopsies. Here, we investigated the performance of this test in an independent dataset of prostate cancers consecutively accrued. Methods: Two board-certified pathologists retrospectively reviewed 600 digitized hematoxylin-and-eosin (H&E) stained diagnostic prostate core needle biopsy slides from 100 consecutive patients, originally diagnosed at an independent hospital. Pathologists’ assessments were based on the H&E image alone; if further testing would be preferred, it was noted in the review notes. All images were assessed by Paige Prostate 1.0, an AI-based diagnostic tool; based on its outputs (either suspicious for cancer or not), the discordant images were re-reviewed by the pathologists and, in parallel, adjudicated with additional testing (e.g. ancillary immunohistochemical markers). Results: Paige Prostate's slide-level sensitivity was 98.9% and its specificity was 93.3% (100% and 78.0%, respectively, at the subject-level). The pathologists' average slide-level sensitivity and specificity without Paige Prostate was 90.9% and 98.6%, respectively. The sensitivity with their consensus read and Paige Prostate increased by 5.7% to 96.6% with only 0.8% decrease in specificity. In addition to new slide-level findings, benefits were also observed at the subject-level; with Paige, three new prostate cancer cases were discovered that were initially missed. Conclusions: The study reflects the potential benefits of the Paige Prostate system in the hands of experienced pathologists and validates the algorithm in a completely independent dataset. Paige Prostate can improve pathologists' sensitivity when reviewing digitized H&E prostate needle biopsy images with a minor impact on specificity.