Abstract
BACKGROUND: This study aimed to evaluate the relationship between surrogate efficacy outcomes and overall survival (OS) in clinical trials for recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC), and to develop a predictive model for OS that incorporates these surrogate outcomes while accounting for baseline patient characteristics. MATERIALS AND METHODS: Data were systematically collected from first-line trials published between January 2010 and March 2025 for R/M HNSCC. Five machine learning models were assessed to predict OS based on surrogate outcomes [objective response rate (ORR), disease control rate, progression free survival (PFS), duration of response, 1-year OS rate] and patient characteristics [human papillomavirus (HPV) status, Eastern Cooperative Oncology Group (ECOG) performance status, programmed death-ligand 1 (PD-L1) expression]. Retrospective data from a single institution was utilized to create simulated datasets for additional validation. RESULTS: Analysis included 90 treatment arms [26 immune checkpoint inhibitor (ICI)-based and 64 non-ICI], extracted from 52 publications. The strongest correlation with median OS was the 1-year OS rate (r = 0.87, P < 0.001). ORR and median PFS showed positive correlations with OS overall, but these correlations were not significant within the ICI subgroup. The Elastic Net model demonstrated strong performance on the held-out test set (r = 0.74, P < 0.001) and the simulated validation set (r = 0.75, P < 0.001). Model interpretation showed that 1-year OS rate and ORR had the strongest impact on predicted OS among surrogate outcomes. Among patient characteristics, the proportion of ECOG 0 and HPV positivity impacted predicted OS across all regimens, while PD-L1 positivity impacted OS only in ICI-based regimens. CONCLUSION: The Elastic Net model effectively bridges surrogate efficacy endpoints and median OS, facilitating the interpretation of early clinical trial outcomes and assisting in the prediction of OS benefit in R/M HNSCC.