Comprehensive Summary
This study developed a novel AI approach to predict Gleason scores by training the AI model on over 1,000 tissue microarray core images, each annotated by 54 pathologists using standardized guidelines. In particular, this approach allows for AI to contribute to primary prostate cancer diagnoses through Gleason scoring in a way that resembles the accepted clinical decision-making process for such diagnoses. The researchers found that this AI model achieved comparable scores to the typical diagnoses approach (Dice score: 0.713 vs. 0.691). Overall, this paper provides an important overview of a more clinically trained AI model that was found to produce comparable or superior Gleason scores, providing insight on both a different tool for diagnosis as well as the factors taken into consideration when formulating Gleason scores by pathologists.
Outcomes and Implications
The Gleason score is used to assess tumor aggressiveness and prognosis in patients with prostate cancer. A major limitation of the Gleason scoring system is the interobserver variability, a product of sampling bias and the subjectivity of tumor build. This study developed a novel AI approach in predicting Gleason score and, thus, contributes to prostate cancer diagnosis. Because the study found that this model is comparable/superior to the current method for Gleason score calculation, it may be used as a tool in the future by healthcare providers to more reliably predict Gleason score. It may also be used alongside the traditional Gleason score calculation to provide additional insight into sources of variability and highlight factors that may be overlooked in manual assessments. As a result, this AI method may improve diagnostic accuracy on a patient-to-patient basis, potentially improving treatment methods and ensuring optimal care is provided.