Exploring patient stratification in head and neck squamous cell carcinoma using machine learning techniques: Preliminary results

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) presents a significant challenge in oncology due to its inherent heterogeneity. Traditional staging systems, such as TNM (Tumor, Node, Metastasis), provide limited information regarding patient outcomes and treatment responses. There is a need for a more robust system to improve patient stratification. Method: In this study, we utilized advanced statistical techniques to explore patient stratification beyond the limitations of TNM staging. A comprehensive dataset, including clinical, radiomic, genomic, and pathological data, was analyzed. The methodology involved correlation analysis of variable pairs and triples, followed by clustering techniques. Results: The analysis revealed that HNSCC subpopulations exhibit distinct characteristics, which challenge the conventional one-size-fits-all approach. Conclusion: This study underscores the potential for personalized treatment strategies based on comprehensive patient profiling, offering a pathway towards more individualized therapeutic interventions.