Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma

Abstract

We employed a mechanistic learning approach, integrating on-treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post-progression survival (PPS)—the duration from the time of documented disease progression to death—and overall survival (OS) in Head and Neck Squamous Cell Carcinoma (HNSCC). We compared the predictive power of model-derived TK parameters versus RECIST and assessed the efficacy of nine TK-OS ML models against conventional survival models. Data from 526 advanced HNSCC patients treated with chemotherapy and cetuximab in the TPExtreme trial were analyzed using a double-exponential model. TK parameters from the first line and maintenance (TKL1) or after four cycles (TK4) were used to predict PPS and post-cycle 4 OS (OS4), combined with 12 baseline parameters. While ML algorithms underperformed compared to the Cox model for PPS, a random survival forest was superior for OS prediction using TK4 and surpassed RECIST-based metrics. This model demonstrated unbiased OS4 prediction, suggesting its potential for improving HNSCC treatment evaluation. Trial Registration: ClinicalTrials.gov identifier: NCT02268695.