EXPLAINABLE AI FOR RISK STRATIFICATION OF HEAD AND NECK CANCER PATIENTS USING TUMOR GENOMICS AND IMAGING BIOMARKERS IN PRECISION ONCOLOGY

Abstract

Head and neck cancer is a very heterogeneous disease with marked variations in aggressiveness, response to therapy, recurrence rate and survival. Traditional risk stratification methods include clinicopathological characteristics, such as stage of the tumor, the presence of nodes, histological grade and treatment history, but these factors do not necessarily reflect the biological heterogeneity of the disease course. The authors of this paper introduce an explainable artificial intelligence approach to the risk stratification in head and neck cancer patients by combining tumor genomics and imaging biomarkers. The study emphasizes the need for the molecular data, including mutation data, gene expression signatures and genomic data, in addition to the radiomic and imaging-derived data obtained from imaging exams. The framework uses machine learning and deep learning techniques to classify patients into clinically relevant groups based on prognosis and recurrence, probability of survival, treatment resistance, and disease progression. Explainability methods are used to highlight the genomic and imaging features which are most influential to the model predictions, to enhance clinical trust and transparency. The proposed solution can help enable personalized oncology, leading to more accurate predictions, an individual treatment plan and better clinical decisions. In conclusion, the study underscores the promise of Explainable AI in connecting multimodal cancer data and precision medicine in the context of head and neck oncology.