Optimizing adjuvant treatment options for patients with glioblastoma

• Published in: Frontiers in neurology 2024-02, Vol.15, p.1326591-1326591
• Main Authors: Zhu, Enzhao, Wang, Jiayi, Shi, Weizhong, Jing, Qi, Ai, Pu, Shan, Dan, Ai, Zisheng
• Format: Article
• Language: English
• Subjects: chemoradiotherapy; deep learning; glioblastoma; machine learning; Neurology; radiotherapy
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2024.1326591

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. 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 ), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT). The Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48-0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55-0.78; dRMST: 7.92, 95% CI, 7.81-8.15; NNT: 1.67, 95% CI, 1.24-2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (