Throughout history advancements in technology have played a significant role in how we live our lives. It has continuously aided in healthcare breakthroughs and holds significant potential for the future. Researchers at Sunnybrook are using emerging artificial intelligence (AI) technologies to advance the diagnosis, treatments and outcomes of some of the world’s most debilitating diseases, like cancer.
Prostate cancer is the most common cancer in Canadian men, with more than 27,000 Canadians being diagnosed with the disease each year. In many cases, prostate cancer develops slowly and can be successfully removed or managed before it spreads to other parts of the body. However, like most types of cancers, there is a risk of prostate cancer to spread or recur after removal or treatment. Traditionally pathologists and clinicians determine cancer recurrence by manually analyzing different images or biosamples.
Matthew McNeil is a senior PhD student working in senior scientist Dr. Anne Martel’s lab at Sunnybrook Research Institute (SRI) where he is developing tools to support the automatic prediction of cancer recurrence. This fall, Matthew was the winner of the Leopard Challenge, a global AI competition. Specifically, the challenge focused on yielding deep learning solutions to predict the time to biochemical recurrence of prostate cancer from H&E-stained histopathological tissue sections.
Matthew is developing AI models that quickly detect features, like Gleason patterns, on slides that pathologists typically use to determine prognosis. The Gleason classification system is used to grade prostate cancer. The scale looks at how abnormal glands in the prostate look and helps determine how likely the cancer is to grow and spread.
“By having these types of patterns highlighted automatically, pathologists will be able to analyze slides more quickly and effectively,” explains Matthew. “My model also has the potential to provide clinicians and patients with a better understanding of the cancer’s risk.”
The model is capable of quickly generating risk scores for patients with prostate cancer. This has the potential to advance patient care and outcomes by supporting more personalized treatment plans as generating these scores can help both clinicians and patients make more informed care decisions.
Matthew is hoping to apply the model he developed during the Leopard Challenge to other datasets for different types of cancers and see his work expand to clinical settings.
