AI enables large-scale brain tumor study, without sharing patient data

Researchers at Penn Medicine and Intel Corporation led the largest-to-date global machine learning effort to securely aggregate knowledge from brain scans of 6,314 glioblastoma (GBM) patients at 71 sites around the globe and develop a model that can enhance identification and prediction of boundaries in three tumor sub-compartments, without compromising patient privacy. Their findings were published today in Nature Communications.

“This is the single largest and most diverse dataset of glioblastoma patients ever considered in the literature, and was made possible through federated learning,” said senior author Spyridon Bakas, PhD, an assistant professor of Pathology & Laboratory Medicine, and Radiology, at the Perelman School of Medicine at the University of Pennsylvania. “The more data we can feed into machine learning models, the more accurate they become, which in turn can improve our ability to understand, treat, and remove glioblastoma in patients with more precision.”

Researchers studying rare conditions, like GBM, an aggressive type of brain tumor, often have patient populations limited to their own institution or geographical location. Due to privacy protection legislation, such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA) in the United States, and General Data Protection Regulation (GDPR) in Europe, data sharing collaborations across institutions without compromising patient privacy data is a major obstacle for many healthcare providers.

A newer machine learning approach, called federated learning, offers a solution to these hurdles by bringing the machine learning algorithm to the data instead of following the current paradigm of centralizing data to the algorithms. Federated learning — an approach first implemented by Google for keyboards’ autocorrect functionality — trains a machine learning algorithm across multiple decentralized devices or servers (in this case, institutions) holding local data samples, without actually exchanging them. It has been previously shown to allow clinicians at institutions in different countries to collaborate on research without sharing any private patient data.

Bakas led this massive collaborative study along with first authors Sarthak Pati, MS, a senior software developer at Penn’s Center for Biomedical Image Computing & Analytics (CBICA), Ujjwal Baid, PhD, a postdoctoral researcher at CBICA, Brandon Edwards, PhD, a research scientist at Intel Labs,and Micah Sheller, a research scientist at Intel Labs.

“Data helps to drive discovery, especially in rare cancers where available data can be scarce. The federated approach we outline allows for access to maximal data while lowering institutional burdens to data sharing.” said Jill Barnholtz-Sloan, PhD, adjunct Professor at Case Western Reserve University School of Medicine.

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