Case Western Reserve University, Cleveland Clinic use AI, radiological MRI scans, and genomics to determine relative life expectancy of glioblastoma patients
Glioblastoma is an aggressive, killer disease. While victims of this fast-moving brain tumor comprise only about 15% of all people with brain cancer, its victims rarely survive more than a few years after diagnosis.
But research scientists and doctors from the
Case Western Reserve University School of Medicine, Case School of Engineering
and Cleveland Clinic have blended two very different types of analysis to
better understand and combat the brain cancer.
The researchers used the tools of Artificial
Intelligence (AI)—in this case, computer image analysis of the initial MRI
scans taken of brain cancer patients—and compared that image analysis with genomic
research to analyze the cancer.
The result: A new and more accurate way to not only determine the relative life expectancy of glioblastoma victims—but identify who could be candidates for experimental clinical drug trials, said Pallavi Tiwari, an assistant professor of biomedical engineering at Case Western Reserve with dual appointments in the School of Medicine and Case School of Engineering.
The AI model used by the researchers leveraged
features from the region adjacent to the tumor, as well as inside the tumor to
identify which patients had a poor prognosis, Pallavi said. Then, they used
gene-expression information to shed light on which biological pathways were
associated with those images.
“Our results demonstrated that image features associated with poor prognosis
were also linked with pathways that contribute to chemo-resistance in glioblastoma.
This could have huge implications in designing personalized treatment decisions
in glioblastoma patients, down the road.” she said.
“While we’re just at the beginning, this is a big step, and someday it could mean that if you have glioblastoma, you could know whether you’ll respond to chemotherapy well or to immunotherapy, based on a patient’s image and gene profiles,” said Manmeet Ahluwalia, MD, Miller Family Endowed Chair of NeuroOncology at the Burkhardt Brain Tumor and Neuro-Oncology Center at Cleveland Clinic, and a co-author of the study.
Beig said the researchers were able to compare
the MRI scans of patients’ tumors with the corresponding genomic information
about that same patient, drawn from a National Institutes of Health database.
“That’s why this study is unique,” she said.
“Most researchers look at one or the other, but we looked at both the MRI
features and the gene expression in conjunction.”
“We can tell you who is at a better risk of
survival,” Beig said. “What clinicians want to do is give their patient an idea
of quality of life, and since roughly 10% of these patients go on to live more
than three years, that’s important information.”