Machine Learning in Neuro-Oncology: Can Data Analysis From 5346 Patients Change Decision-Making Paradigms?

Background: Machine learning (ML) is an application of artificial intelligence (AI) that gives computer systems the ability to learn data, without being explicitly programmed. Currently, ML has been successfully used for optical character recognition, spam filtering, and face recognition. The aim of the present study was to review the current applications of ML in the field of neuro-oncology. Methods: We conducted a systematic literature review using the PubMed and Cochrane databases using a keyword search for January 30, 2000 to March 31, 2018. The data were clustered for neuro-oncology scope of ML into 3 categories: patient outcome predictors, imaging analysis, and gene expression. Results: Data from 5346 patients in 29 studies were used to develop ML-based algorithms (MLBAs) in neuro-oncology. MLBAs were used to predict the outcomes for 2483 patients, with a sensitivity range of 78%–98% and specificity range of 76%–95%. In all studies, the MLBAs had greater accuracy than the conventional ones. MLBAs for image analysis showed accuracy in diagnosing low-grade versus high-grade gliomas, ranging from 80% to 93% and 90% for diagnosing high-grade glioma versus lymphoma. Seven studies used MLBAs to analyze gene expression in neuro-oncology. Conclusions: MLBAs in neuro-oncology have been shown to predict patients’ outcomes more accurately than conventional parameters in a retrospective analysis. If their high diagnostic accuracy in imaging analysis and detection of somatic mutations are corroborated in prospective studies, the use of tissue diagnosis or liquid biopsy might be curtailed. Finally, MLBAs are promising to help guide targeted therapy, can lead to personalized medicine, and open areas of study in the cancer cellular signaling system, not otherwise known.