AI insiders celebrated a massive new study highlighting a federated learning AI model’s ability to delineate glioblastoma brain tumors with high accuracy and generalizability, while demonstrating FL’s potential value for rare diseases and underrepresented populations.
The UPenn-led research team went big, as the study’s 71 sites in 6 continents made it the largest FL project to-date, its 6,314 patients’ mpMRIs created the biggest glioblastoma (GBM) dataset ever, and its nearly 280 authors were the most we’ve seen in a published study.
The researchers tested their final GBM FL consensus model twice – first using 20% of the “local” mpMRIs from each site that weren’t used in FL training, and second using 590 “out-of-sample” exams from 6 sites that didn’t participate in FL development.
These FL models achieved significant improvements compared to an AI model trained with public data for delineating the three main GBM tumor sub-compartments that are most relevant for treatment planning.
- Surgically targetable tumor core: +33% w/ local, +27% w/ out-of-sample
- Enhancing tumor: +27% w/ local, +15% w/ out-of-sample
- Whole tumor: +16% w/ local, +16% w/ out-of-sample data
The Takeaway
Federated learning’s ability to improve AI’s performance in new settings/populations while maintaining patient data privacy has become well established in the last few years. However, this study takes FL’s resume to the next level given its unprecedented scope and the significant complexity associated with mpMRI glioblastoma exams, suggesting that FL will bring a “paradigm shift for multi-site collaborations.”
