The promise of using AI to help prostate cancer care

In 2021, nearly 250,000 Americans will be diagnosed with prostate cancer, which remains the second most common cancer among men in the U.S. Even as we make advancements in cancer research and treatment, diagnosing and treating prostate cancer remains difficult. This National Prostate Cancer Awareness Month, we’re sharing how Google researchers are looking at ways artificial intelligence (AI) can improve prostate cancer care and the lessons learned along the way.  

Our AI research to date 

Currently, pathologists rely on a process called the ‘Gleason grading system’ to grade prostate cancer and inform the selection of an effective treatment option. This process involves examining tumor samples under a microscope for tissue growth patterns that indicate the aggressiveness of the cancer. Over the past few years, research teams at Google have developed AI systems that can help pathologists grade prostate cancer with more objectivity and ease. 

These AI systems can help identify the aggressiveness of prostate cancer for tumors at different steps of the clinical timeline — from smaller biopsy samples during initial diagnosis to larger samples from prostate removal surgery. In prior studies published in JAMA Oncology and Nature Partner Journal Digital Medicine, we found our AI system for Gleason grading prostate cancer samples performed at a higher rate of agreement with subspecialists (pathologists who have specialized training in prostate cancer) as compared to general pathologists. These results suggest that AI systems have the potential to support high-quality prostate cancer diagnosis for more patients. 

To understand this system's potential impact within a clinical workflow, we also studied how general pathologists could use our AI system during their assessments. In arandomized study involving 20 pathologists reviewing 240 retrospective prostate biopsies, we found that the use of an AI system as an assistive tool was associated with an increase in grading agreement between general pathologists and subspecialists. This indicated that AI tools may help general pathologists grade prostate biopsies with greater accuracy. The AI system also improved both pathologists’ efficiency and their self-reported diagnostic confidence. 

In our latest study in Nature Communications Medicine, we directly examined whether the AI’s grading was able to identify high-risk patients by comparing the system’s grading against mortality outcomes. This is important because mortality outcomes are one of the most clinically relevant results for evaluating the value of Gleason grading, ensuring greater confidence in the AI’s grading. We found that the AI’s grades were more strongly associated with patient outcomes than the grades from general pathologists, suggesting that the AI could potentially help inform decision-making on treatment plans. 

Contributing to reducing variability in AI research 

We first began training our AI system using Gleason grades from both general pathologists and subspecialists. As we continued to develop AI systems for assisting prostate cancer grading, we learned that both training the AI and evaluating the model’s performance can be challenging because often the “ground truth” or reference standard is based on expert opinion. Because of this subjectivity, for some cases, two pathologists examining the same sample may arrive at a different Gleason grade.

To improve the quality of the “ground truth”, we developed a set of best practices that we have shared this week in Lancet Digital Health. These recommendations include involving experienced prostate pathology experts, making sure that multiple experts look at each sample, and designing an unbiased disagreement resolution process. By sharing these learnings, we hope to encourage and accelerate further work in this area, particularly in earlier-phase research when it’s impractical to train or validate a model using patient outcomes data.

Our research has shown that AI can be most helpful when it's built to support clinicians with the right problem, in the right way, at the right time. With that in mind, we plan to further validate the role of AI and other novel technologies in helping improve prostate cancer diagnosis, treatment planning and patient outcomes.