AI-based model predicts glioblastoma recurrence to guide targeted treatment

Using unprocessed tissue saves time and effort

The researchers analyzed tissue samples collected during glioblastoma surgery from UCSF Health patients, for whom the median time to recurrence was 5.5 months. They developed their model using about 300 samples from 60 patients and tested it separately on about 100 samples from another 20 patients.

"The overall goal was to delay that first recurrence," said co-senior author Todd Hollon, MD, of the Machine Learning in Neurosurgery Laboratory at the University of Michigan, Ann Arbor. "Ultimately, we hope that extending that window could translate into longer survival."

Source:

University of California - San Francisco

Journal reference:

https://www.science.org/doi/10.1126/sciadv.aec8202