AI predicts meningioma recurrence from routine pathology slides

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 which provides valuable diagnostic and prognostic information but can be costly, time-consuming and is unavailable in many hospitals.

Making advanced tumor insights more accessible

Molecular testing can help identify which tumors are more likely to recur and which may respond differently to treatment. But these tests require specialized technology and expertise, limiting access for many patients.

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 were able to classify meningioma subtypes and predict recurrence risk using standard pathology slides that are already part of routine patient care.

The findings suggest that AI could one day help clinicians obtain more detailed tumor information without requiring patients to undergo advanced genetic testing.

Helping guide treatment decisions

For patients with meningiomas, 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 was able to remove the tumor and patient age.

Researchers also found that the AI models could identify patterns of tumor heterogeneity - differences within the same tumor - that may help explain why some tumors behave more aggressively or respond differently to treatment.

The researchers note that additional prospective studies are needed before the AI models can be used routinely in clinical care. Still, they say the findings lay the groundwork for more accessible, personalized care for patients with meningiomas - and potentially for similar AI approaches in other cancers.

"The aim is to make these algorithms readily and simply accessible for use globally, improving patient care across many healthcare settings," says Dr. Zadeh.

Source:

Mayo Clinic

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