AUTHOR=Zhu Enzhao , Wang Jiayi , Shi Weizhong , Jing Qi , Ai Pu , Shan Dan , Ai Zisheng 

TITLE=Optimizing adjuvant treatment options for patients with glioblastoma

JOURNAL=Frontiers in Neurology

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2024.1326591

DOI=10.3389/fneur.2024.1326591

ISSN=1664-2295

ABSTRACT=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).