Machine Learning Enhances Ability To Predict Survival From Brain Tumors

MedicalResearch.com Interview with:

Lee Cooper, Ph.D. Assistant Professor of Biomedical Informatics Assistant Professor of Biomedical Engineering Emory University School of Medicine - Georgia Institute of Technology

Dr. Cooper

Lee Cooper, Ph.D.
Assistant Professor of Biomedical Informatics
Assistant Professor of Biomedical Engineering
Emory University School of Medicine – Georgia Institute of Technology

MedicalResearch.com: What is the background for this study? What are the main findings? 

Response: Gliomas are a form of brain tumor that are often ultimately fatal, but patients diagnosed with glioma may survive as few as 6 months to 10 or more years. Prognosis is an important determinant in selecting treatment, that can range from simply monitoring the disease to surgical removal followed by radiation treatment and chemotherapy. Recent genomic studies have significantly improved our ability to predict how rapidly a patient’s disease will progress, however a significant part of this determination still relies on the visual microscopic evaluation of the tissues by a neuropathologist. The neuropathologist assigns a grade that is used to further refine the prognosis determined by genomic testing.

We developed a predictive algorithm to perform accurate and repeatable microscopic evaluation of glioma brain tumors. This algorithm learns the relationships between visual patterns presented in the brain tumor tissue removed from a patient brain and the duration of that patient’s survival beyond diagnosis. The algorithm was demonstrated to accurately predict survival, and when combining images of histology with genomics into a single predictive framework, the algorithm was slightly more accurate than models based on the predictions of human pathologists. We were also able to identify that the algorithm learns to recognize some of the same tissue features used by pathologists in evaluating brain tumors, and to appreciate their prognostic relevance. Continue reading