Mayo Clinic researchers and collaborators showed that artificial intelligence can analyze routine pathology slides to help classify meningiomas, the most common primary brain tumor in adults, and predict a patient’s risk of tumor recurrence. The study, published in The Lancet Digital Health, demonstrated that deep learning models can extract molecular and prognostic information from standard hematoxylin and eosin, or H&E, slides, which are already used in routine clinical care. These insights are typically obtained through DNA methylation profiling, an advanced genetic test that can be costly, time-consuming and unavailable in many hospitals. Using tissue samples, pathology images and clinical data from 672 patients, researchers trained AI to uncover information about a tumor’s biology. Drawing on multiple de-identified datasets, including data resources from Mayo Clinic Platform, the models classified meningioma subtypes and predicted recurrence risk using standard pathology slides. The findings suggest AI could help clinicians obtain more detailed tumor information without requiring patients to undergo advanced genetic testing. For patients, recurrence risk can influence follow-up care, imaging frequency and whether radiation therapy should be considered. The study found that AI-based predictions remained useful even after accounting for traditional clinical factors such as tumor grade, the extent to which surgery removed the tumor and patient age. Researchers also found the AI models could identify patterns of tumor heterogeneity, or differences within the same tumor, that may help explain why some tumors behave more aggressively or respond differently to treatment. The researchers noted that additional prospective studies are needed before the AI models can be used routinely