New deep learning tool makes advanced brain tumour analysis more accessible.

New AI tool predicts brain tumour recurrence risk without costly genetic testing

New AI tool predicts brain tumour recurrence risk by analysing routine pathology slides, offering an alternative to costly genetic testing for many patients with meningiomas. Developed by researchers at Mayo Clinic and collaborators, the deep learning model was trained using pathology images, tissue samples and clinical data from 672 patients. The system extracts molecular and prognostic information from standard H&E pathology slides, which are already used in routine clinical practice, reducing dependence on expensive DNA methylation profiling.

The study, published in The Lancet Digital Health, found that the AI model accurately classified meningioma subtypes and predicted tumour recurrence risk even after accounting for conventional clinical factors such as tumour grade, patient age and extent of surgical removal.

Researchers believe the technology could improve access to personalised cancer care, particularly in hospitals where advanced molecular testing is unavailable. Although further prospective validation is required before clinical adoption, the findings demonstrate how digital pathology and artificial intelligence can make sophisticated diagnostic insights more accessible while supporting, rather than replacing, physician decision-making.

Key Takeaways

AI can predict meningioma recurrence risk using routine pathology slides.
The model was trained and validated using data from 672 patients.
The technology reduces reliance on expensive DNA methylation testing.
AI predictions remained valuable alongside traditional clinical risk factors.
Further clinical validation is required before routine medical use.

AI transforms brain tumour diagnosis through routine pathology imaging

Trained on data from 672 patients, the tool uses routine pathology images to provide insights that currently often require advanced genetic testing, supporting treatment decisions and patient follow-up.

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 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.

“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 the David C and Flora C Pratt Distinguished Chief Medical Officer for Mayo Clinic Platform.

Making advanced tumour 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 specialised 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, personalized care for patients with meningiomas and potentially for similar AI approaches in other cancers.

As with any clinical decision-support tool, the researchers emphasize 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.

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