Using Connectomics and Machine Learning to Predict Survival in Diffuse Glioma

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ABSTRACT Diffuse gliomas are the most common malignant primary brain tumors. Clinical outcomes, including overall survival, vary significantly across individual patients and are not adequately explained by known prognostic factors such as age, histologic and molecular pathology, and imparted treatments. A deeper understanding of prognostic influences in diffuse glioma can facilitate therapeutic decision making and patient counselling and lend further insight into the biologic underpinnings of the disease. Magnetic resonance imaging (MRI) scans of the brain are part of standard pre-operative evaluation in these patients. As brain structure and function are modulated by biologic and environment factors, MRI-derived metrics often provide sensitive prognostic biomarkers. We have preliminarily shown that connectomics, a method of measuring brain connectivity from MRI, can accurately predict survival in patients with diffuse glioma. We will retrospectively obtain at least 1,150 datasets and attempt to validate our preliminary models (Aim 1). We will also examine connectome phenotypes associated with tumor genotypes to help refine and improve our models (Aim 2). Accurate pre- operative prediction of outcome may ultimately allow for better tailored interventions for the individual patient and assist clinicians in optimizing both tumor control and neurologic function in treatment decision making.