IMPROVING GLIOBLASTOMA PROGNOSIS WITH MRI-BASED MACHINE LEARNING

USING TENSORFLOW AND KERAS ON DIAGNOSTIC IMAGING DATASETS TO TRAIN AND BUILD MODELS

Abstract

A malignant tumor in the brain is a life-threatening condition. Known as glioblastoma, it's both the most common form of brain cancer in adults and the one with the worst prognosis, with median survival being less than a year. More than 300,000 people each year are diagnosed with glioblastoma according to the UT Southwestern Medical Center, and the average life expectancy of patients with glioblastoma is only 8 months (NBTS, 2023). The presence of a specific genetic sequence in the tumor known as MGMT promoter methylation has been shown to be a favorable prognostic factor and a strong predictor of responsiveness to chemotherapy. MGMT stands for O (6)-Methylguanine-DNA-methyltransferase, it is a critical enzyme involved in DNA repair mechanisms in the cell. This project explores training and testing of Machine Learning Models using MRI (magnetic resonance imaging) scans to detect for the presence of MGMT promoter methylation. In this specific research, a Keras based model was used for optimization. The resulting model can predict the genetic subtype of glioblastoma, leading to fewer surgeries and better treatment decisions. By using this method we can cut unnecessary surgeries, help decide the type of therapy needed, and overall focus on improving the management, survival, and prospects of patients with glioblastoma. The model's performance metrics show an accuracy of 0.479, sensitivity (recall) of 0.666, specificity of 0.301, and a precision of 0.476.