Adaptive Fine-tuning based Transfer Learning for the Identification of MGMT Promoter Methylation Status

Abstract

Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis. Treatment usually includes a mix of surgical resection, radiation therapy, and akylating chemotherapy but, even with these intensive treatments, the 2-year survival rate is still very low. O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to chemotherapy, but it is invasive and time-consuming to determine the methylation status. Due to this, there has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients. We developed a 3D SpotTune network with adaptive fine-tuning capability to improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status. Using the pretrained weights of MedicalNet coupled with the SpotTune network, we compared its performance with two equivalent networks: one that is initialized with MedicalNet weights, but with no adaptive fine-tuning and one initialized with random weights. These three networks are trained and evaluated using the UPENN-GBM dataset, a public GBM dataset provided by the University of Pennsylvania. The SpotTune network enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using MRIs as compared to using a network with randomly initialized weights.