By Katelin Shaft
Mayo Clinic researchers and collaborators have shown that an artificial intelligence (AI) tool can analyse routine pathology slides to help clinicians classify meningiomas, the most common primary brain tumour in adults, and better understand a patient’s risk of tumour recurrence.
The study, published in The Lancet Digital Health, demonstrates that deep learning models can support the extraction of molecular and prognostic information from standard haematoxylin 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.
“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 into AI algorithms,” says Gelareh Zadeh, MD, PhD, chair of the Department of Neurologic Surgery at Mayo Clinic in Rochester and Chief Medical Officer for Mayo Clinic Platform.
Making advanced tumor insights more accessible
Meningiomas can vary widely in behaviour. Some grow slowly and may never return after treatment, while others are more aggressive and more likely to recur. Understanding that risk is critical for patients and care teams deciding whether additional treatment, such as radiation therapy, may be needed after surgery.
Molecular testing can help identify which tumours 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 developed and tested AI models designed to help identify patterns linked to a tumour’s biology. Drawing on multiple de-identified datasets, including data resources from Mayo Clinic Platform, the models supported classification of meningioma subtypes and recurrence risk prediction using standard pathology slides that are already part of routine patient care.
The findings suggest that, with further validation, AI-based tools could one day help clinicians obtain more detailed tumour information to inform patient care, without requiring every patient 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 tumour grade, the extent to which surgery was able to remove the tumour and patient age.
Researchers also found that the AI models could identify patterns of tumour heterogeneity – differences within the same tumour – that may help explain why some tumours 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, personalised care for patients with meningiomas – and potentially for similar AI approaches in other cancers.
As with any clinical decision-support tool, the researchers emphasise that these models would require rigorous evaluation, validation and ongoing physician oversight before being considered for routine care. “The aim is to make these algorithms readily and simply accessible for use globally, improving patient care across many healthcare settings,” says Dr Zadeh.
For a complete list of authors, disclosures and funding, review the publication.
Source: Mayo Clinic
