The study, published in The Lancet Digital Health, demonstrates that deep learning models can extract molecular and prognostic information from standard hematoxylin and eosin, or H&E, slides—the same type of tissue images already used in routine clinical care.

These insights are typically obtained through DNA methylation profiling, an advanced genetic test that provides valuable diagnostic and prognostic information but can be costly, time-consuming and unavailable in many hospitals.

“This is one of the many studies where we can harness the strength of digital pathology by capturing the last two decades of genomic and molecular knowledge in AI algorithms,” says Gelareh Zadeh, M.D., Ph.D., chair of the Department of Neurologic Surgery at Mayo Clinic in Rochester and the David C. and Flora C. Pratt Distinguished Chief Medical Officer for Mayo Clinic Platform.