06.12.2024

New recommendations, published in The Lancet Oncology, provide new guidance for the use of artificial intelligence (AI) in the diagnosis and treatment of brain cancer. The goal is to enable more objective tumor measurements and improve patient care through standardized AI applications.

Image: Person standing in front of a large screen with brain scans; Copyright: Liz Kaye, Indiana University

The new recommendations are a much-needed update to the current standard of care.

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AI for objective tumor assessment

Spyridon Bakas, lead author of the second policy review and professor at Indiana University School of Medicine, explains: "We can use AI to look at images of the tumors more objectively." AI programs can quickly determine the type of tumor, its subtype, and its grade, as well as track the tumor’s progression during treatment. This could lead to more accurate diagnoses and consistent treatment plans.

Benefits and challenges of AI in oncology

The research team emphasizes that predictive, prognostic, and diagnostic AI models are becoming increasingly available to healthcare providers. However, their use varies significantly across institutions. Raymond Y. Huang, associate professor at Harvard Medical School, adds, "However, there needs to be a standardized way to use AI to accurately diagnose and treat patients."

Key recommendations of the guidelines

The guidelines provided by the expert team include several key recommendations:

Use AI software developed with large, diverse patient datasets.

Ensure that AI models for tumor definition follow World Health Organization (WHO) criteria.

Consider how tumor images are acquired, processed, and segmented before analysis.

Thomas Booth, co-author from King’s College London, states, "These guidelines are critical for ensuring that AI tools developed in the U.K. and beyond meet rigorous standards and improve patient outcomes."

Ongoing research and the path forward

While these guidelines are among the first in the world regarding AI's use in cancer care, further research is needed. Bakas underscores: “It is important that we continue our study of these AI models on large, diverse patient populations to continue extending our understanding of disease and improving the way we use them.”

MEDICA-tradefair.com; Source: Indiana University

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