Optimizing adjuvant treatment options for patients with glioblastoma - Zhu, Enzhao; Wang, Jiayi; Shi, Weizhong; Jing, Qi; Ai, Pu; Shan, Dan; Ai, Zisheng (Frontiers in Neurology, 2024)
Background: This study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method.Methods: We trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HR a ), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT).