• Yang Li, Qinyu Xiao, Haiqi Chen, Enzhao Zhu, Xin Wang, Jianmeng Dai, Xu Zhang, Qiuyi Lu, Yanming Zhu, and Guangliang Yang.
• Heilongjiang University of Chinese Medicine, Harbin, China.
• Medicine (Baltimore). 2024 Sep 13; 103 (37): e39659e39659.

AbstractTo assess deep learning models for personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy for elderly head and neck squamous cell carcinoma (HNSCC) patients who are not surgery candidates. A comparison was made between patients whose treatments aligned with model recommendations and those whose did not, using overall survival as the primary metric. Bias was addressed through inverse probability treatment weighting (IPTW), and the impact of patient characteristics on treatment choice was analyzed via mixed-effects regression. Four thousand two hundred seventy-six elderly HNSCC patients in total met the inclusion criteria. Self-Normalizing Balanced individual treatment effect for survival data model performed best in treatment recommendation (IPTW-adjusted hazard ratio: 0.74, 95% confidence interval [CI], 0.63-0.87; IPTW-adjusted risk difference: 9.92%, 95% CI, 4.96-14.90; IPTW-adjusted the difference in restricted mean survival time: 16.42 months, 95% CI, 10.83-21.22), which surpassed other models and National Comprehensive Cancer Network guidelines. No survival benefit for chemoradiotherapy was seen for patients not recommended to receive this treatment. Self-Normalizing Balanced individual treatment effect for survival data model effectively identifies elderly HNSCC patients who could benefit from chemoradiotherapy, offering personalized survival predictions and treatment recommendations. The practical application will become a reality with further validation in clinical settings.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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