Abstract
Despite advances in the management of head and neck squamous cell carcinoma (HNSCC), mortality within 6 months of diagnosis remains a substantial clinical challenge. The objectives of this study are: (i) to develop a machine learning (ML) model using data from the Surveillance, Epidemiology, and End Results (SEER) program to assess the influence of patient characteristics, tumor features, and treatment modalities on early mortality in HNSCC; (ii) to explore and compare the prognostic potentials of patient-, tumor, and treatment-related factors across distinct mortality time points-6-month mortality (early mortality), 24-month mortality, and 5-year mortality; and (iii) to externally and independently validate the early mortality model using multicenter data from the Thuringian Cancer Registry (Jena, Germany) and a prospective observational cohort from the Helsinki University Hospital (Helsinki, Finland). We identified 4802 patients with HNSCC from the SEER for model development. Permutation-based feature importance was used to identify risk factors associated with early mortality. External validation was conducted using 1952 cases from the Thuringian Cancer Registry and 58 cases from the Helsinki University Hospital. The ML model achieved a weighted area under curve (AUC) of 0.75 for predicting early mortality in the SEER cohort. External validation yielded weighted AUC values of 0.70 (Germany) and 0.60 (Finland). Aggregate feature importance for early mortality indicated that higher age at diagnosis, presence of earlier primary malignant tumors besides HNSCC, unmarried patients with T1-T3 HNSCC, T3 stage, and having hypopharyngeal or laryngeal cancer, in decreasing order of significance, were important. For 24-month mortality, the associated risk factors in decreasing order of significance were T3 stage, being elderly in terms of age at diagnosis, presence of earlier primary malignant tumors besides HNSCC, having hypopharyngeal or oral cavity cancer, and N3 stage. The associated risk factors for 5-year mortality were found to be the same with those of early mortality with the additional inclusion of T2 stage. Identification of patients at elevated risk of early death supports timely intervention and individualized therapeutic decision-making. The developed ML model identified several risk factors associated with early death and may aid in clinical decision-making with the potential to improve survival outcomes.