Treating a Deadly Brain Tumor Before It Recurs? AI Tool May Help
A new AI-based model can predict where the most common malignant brain cancer, glioblastoma, is most likely to return after initial surgery, potentially giving doctors a chance to treat it before it becomes visible on MRI.
Glioblastoma is the most lethal brain tumor in adults, with a median survival of about 17 months after diagnosis. Even after surgeons remove all visible tumor and patients receive follow-up treatment, the cancer almost always returns.
Now, researchers led by UC San Francisco and University of Michigan have developed an AI approach that can predict, with a high degree of accuracy, where first recurrence is likely to appear. In glioblastoma, most patients experience recurrence at or close to the tumor cavity.
“The system has incredible potential to provide neurosurgeons with valuable real-time guidance during tumor removal,” said first author Sanjeev Herr, MD, a UCSF postdoctoral research fellow. “It can also generate insights that guide subsequent treatment decisions.”
The findings, published Sept. 25 in Science Advances and supported by the National Institutes of Health (NIH), could eventually enable doctors to use targeted treatments in these areas before a new tumor becomes visible on MRI.
“These treatments might include removing additional tissue during the initial surgery when it is safe to do so,” said senior author Shawn Hervey-Jumper, MD, a neurosurgeon at UCSF Health and Mitchel S. Berger MD, Endowed Professor at the Weill Institute for Neurosciences. “Patients with disease in parts of the brain that cannot be removed may undergo other treatments targeted at the predicted sites of progression, like higher-dose focal radiation or drugs infused directly into the tumor, via a catheter placed through the skull.”
Delaying recurrence
For the study, the researchers analyzed tissue samples collected during glioblastoma surgery from UCSF Health patients, for whom the median time to cancer 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 researchers used a technique called stimulated Raman histology (SRH), which produces microscopic images of fresh, unprocessed tissue in less than a minute. Conventional pathology requires tissue to be processed with dyes and stains, which takes longer and is more labor-intensive.
They then analyzed the images with FastGlioma, an AI system developed by researchers at UCSF and University of Michigan. The system scores tissue based on tumor infiltration.
That AI score alone performed about as well as conventional pathology at predicting which areas would later develop recurrent tumor. But the researchers then combined the AI score with clinical, imaging, and molecular data and tested the data using six machine-learning models. The best-performing model was significantly more likely to distinguish between sites that would and would not develop recurrences.
The AI-generated measure of tumor infiltration was also the strongest individual predictor of recurrence in five of the six models, providing more predictive information than the tumor’s molecular characteristics.
The researchers also tested how precisely the model could pinpoint where recurrence would develop. The model performed well at predicting whether cancer would return within 5 or 10 millimeters of the tissue that had been sampled.
The team focused on the first recurrence because patients often receive experimental treatments later in their disease that can influence tumor growth, making subsequent recurrences more difficult to predict reliably.
“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.”
Authors: Please see the study.
Funding: National Institutes of Health (NINDS R01 NS137950, K12NS080223, T32GM007863, NCI P01 CA118816), and additional foundation, philanthropic and institutional sources. There are no disclosures to report.
About UCSF Health: UCSF Health is recognized worldwide for its innovative patient care, reflecting the latest medical knowledge, advanced technologies and pioneering research. It includes the flagship UCSF Medical Center, which is among the nation's top specialty hospitals, as well as UCSF Benioff Children’s Hospitals, with campuses in San Francisco and Oakland; two community hospitals, UCSF Health Stanyan Hospital and UCSF Health Hyde Hospital; Langley Porter Psychiatric Hospital; UCSF Benioff Children’s Physicians; and the UCSF Faculty Practice. These hospitals serve as the academic medical center of the University of California, San Francisco, which is world-renowned for its graduate-level health sciences education and biomedical research. UCSF Health has affiliations with hospitals and health organizations throughout the Bay Area. Visit https://ucsfhealth.org. Follow UCSF Health on Facebook or on LinkedIn.
