TCGA-HNSSC Clinical and Mutation combination

Head and neck squamous cell carcinoma (HNSCC) poses considerable clinical challenges due to its involvement with critical tissues in the head and neck. Despite a high five-year survival rate of 68.5%, it remains a highly prevalent cancer worldwide, necessitating enhanced risk assessment and individualized treatment strategies. This study leverages missense variant data from 492 HNSCC patients in The Cancer Genome Atlas to address these needs. To facilitate a more nuanced approach to personalized risk assessment, we employed a fuzzy logic-based methodology to integrate results from multiple survival analyses. The proposed model combines a Fuzzy Inference System, Convolutional Neural Networks, Random Survival Forests, and Cox Proportional Hazards regression models to identify and evaluate risk factors. We evaluated the model in terms of accuracy, sensitivity, specificity, F1 score, and time-dependent area under the curve. The fuzzy-based survival analysis model had an average accuracy of 92.04%, effectively classifying and identifying missense variants linked to HNSCC mortality. By integrating diverse survival analyses, this study advances personalized medicine in HNSCC, offering valuable insights that could enhance clinical decision-making, optimize treatment strategies, and improve patient outcomes.