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Optimizing adjuvant treatment options for patients with glioblastoma

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Enzhao Zhu
  • Jiayi Wang
  • Weizhong Shi
  • Qi Jing
  • Pu Ai
  • Dan Shan
  • Zisheng Ai
Article number1326591
<mark>Journal publication date</mark>21/02/2024
<mark>Journal</mark>Frontiers in Neurology
Publication StatusPublished
<mark>Original language</mark>English


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 (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT). Results: 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 (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT. Conclusion: Our study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations.