AUTHOR=Zhang Linmei , Zhu Enzhao , Shi Jiaying , Wu Xiao , Cao Shaokang , Huang Sining , Ai Zisheng , Su Jiansheng 

TITLE=Individualized treatment recommendations for patients with locally advanced head and neck squamous cell carcinoma utilizing deep learning

JOURNAL=Frontiers in Medicine

VOLUME=Volume 11 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1478842

DOI=10.3389/fmed.2024.1478842

ISSN=2296-858X

ABSTRACT=BackgroundThe conventional treatment for locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is surgery; however, the efficacy of definitive chemoradiotherapy (CRT) remains controversial.ObjectiveThis study aimed to evaluate the ability of deep learning (DL) models to identify patients with LA-HNSCC who can achieve organ preservation through definitive CRT and provide individualized adjuvant treatment recommendations for patients who are better suited for surgery.MethodsFive models were developed for treatment recommendations. Their performance was assessed by comparing the difference in overall survival rates between patients whose actual treatments aligned with the model recommendations and those whose treatments did not. Inverse probability treatment weighting (IPTW) was employed to reduce bias. The effect of the characteristics on treatment plan selection was quantified through causal inference.ResultsA total of 7,376 patients with LA-HNSCC were enrolled. Balanced Individual Treatment Effect for Survival data (BITES) demonstrated superior performance in both the CRT recommendation (IPTW-adjusted hazard ratio (HR): 0.84, 95% confidence interval (CI), 0.72–0.98) and the adjuvant therapy recommendation (IPTW-adjusted HR: 0.77, 95% CI, 0.61–0.85), outperforming other models and the National Comprehensive Cancer Network guidelines (IPTW-adjusted HR: 0.87, 95% CI, 0.73–0.96).ConclusionBITES can identify the most suitable treatment option for an individual patient from the three most common treatment options. DL models facilitate the establishment of a valid and reliable treatment recommendation system supported by quantitative evidence.