Machine Learning Approaches for Prognostication of Newly Diagnosed Glioblastoma

Abstract

Background: Glioblastoma (GBM) is the poorest prognosis in glioma. Although Temozolomide (TMZ) with radiotherapy following tumor resection is currently the standard treatment, the high cost has become an economic burden in a limited-resource setting. In an era of disruptive innovation, machine learning (ML) has been recently performed to be the clinical prediction tool for prognostication, especially GBM. The aim of the study was to assess the predictability of ML algorithms for 2-year survival in patients with GBM. Methods: A retrospective cohort study was performed in patients with GBM. Various clinical, radiological, and treatment variables were collected, and the outcome was a 2-year living status as bi-classifiers. The candidate variables, which had a P<0.1, were performed to train the ML model. For training the ML model, random forest (RF), logistic regression (LR), and support vector machines were used for training the model and testing the predictive performance. Results: There were 190 GBM patients in the cohort. Four candidate variables were used for building the ML model and testing the performance of each algorithm. The LR and RF algorithms had an acceptable performance for predicting a 2-year survival with an area under the receiver operating characteristic curve at 0.82 and 0.81, respectively. Conclusion: ML-based algorithms had an acceptable performance for the prognostication of 2-year survival in GBM patients that could be implicated in real-world practice for selecting patients with a favorable prognosis and developing treatment strategies for resource allocation in a limited-resource setting.