An Artificial Intelligence-Powered Platform for Prostate Cancer Grading

While the Gleason grading system has been the most reliable tool for the prognosis of prostate cancer since its development, its clinical application remains limited. A study examined the impact of an artificial intelligence (AI)-assisted approach to prostate cancer grading and quantification. The findings were published in JAMA Network Open.

This diagnostic study was conducted from August 2, 2017, to December 30, 2019. The study consisted of 589 men (mean age, 63.8 years) with biopsy-confirmed prostate cancer who received care in the University of Wisconsin Health System between January 1, 2005, and February 28, 2017. Researchers selected and analyzed 1,000 biopsy slides to create digital whole-slide images, which were used to develop and validate a deep convolutional neural network-based AI-powered platform. Subsequently, the whole-slide images were divided into a training set (n=838) and validation set (n=162). The primary outcome was defined as the accuracy of prostate cancer detection by the AI-powered platform and comparison of prostate cancer grading and quantification.

According to the results, the AI system was able to discern prostate cancer from benign prostatic epithelium and stroma with high accuracy at the patch-pixel level, with an area under the receiver operating characteristic curve of 0.92 (95% confidence interval [CI] 0.88-0.95). Moreover, the AI system achieved almost perfect agreement with the training pathologist in detecting prostate cancer at the patch-pixel level (weighted κ = 0.97; asymptotic 95% CI 0.96-0.98) and in grading prostate cancer at the slide level (weighted κ = 0.98; asymptotic 95% CI 0.96-1.00).

“In this diagnostic study, an AI-powered platform was able to detect, grade, and quantify prostate cancer with high accuracy and efficiency and was associated with significant reductions in interobserver variability,” wrote the authors in conclusion. “These results suggest that an AI-powered platform could potentially transform histopathological evaluation and improve risk stratification and clinical management of prostate cancer.”