CT Radiomics-Clinical Model for Noninvasive Prediction of Tumor Mutation Burden and Survival in Locally Advanced Head and Neck Squamous Cell Carcinoma

Purpose Tumor mutation burden (TMB) has emerged as a promising biomarker for predicting immunotherapy response. This study aimed to evaluate the potential of a CT-based radiomics model to predict TMB status and overall survival (OS) in patients with head and neck squamous cell carcinoma (HNSCC). Methods Somatic mutation and transcriptome data of 506 HNSCC cases were retrieved from The Cancer Genome Atlas (TCGA) to calculate TMB and identify differentially expressed genes (DEGs). Functional enrichment analysis was conducted using Gene Ontology (GO) and KEGG databases. Kaplan-Meier and Cox regression analysis was used to assess the prognostic value of TMB. A cohort of 159 patients with pre-treatment contrast-enhanced CT scan from The Cancer Imaging Archive (TCIA) was used to extract radiomics features. The dataset was split into training (n = 112) and validation (n = 47) datasets. Feature selection was performed using univariate Cox regression and LASSO, followed by multivariate Cox analysis. Predictive models (radiomics, clinical, and combined) were evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values. Results High TMB was associated with poor OS. Functional enrichment revealed that DEGs were enriched in immune processes and cell signaling. Notably, 16 features were selected for the TMB prediction model. The combined model achieved AUC values of 0.902 (training) and 0.669 (validation) for TMB prediction. For 1-, 3-, and 5-year OS prediction, the combined model achieved AUC values of 0.665–0.784 in the training cohort and 0.680–0.772 in the validation cohort. Conclusion The CT-derived radiomics model demonstrated potential for noninvasive prediction of TMB and survival in HNSCC, appearing advantageous for immunotherapy decision-making.