Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review

SIMPLE SUMMARY: Head and neck tumors (HNTs) are associated with a high mortality due to their commonly insidious and asymptomatic development. Regarding risk stratification and long-term patient outcome prediction, routine clinical evaluation by radiologists has several limitations. Numerous researchers have assessed the usefulness of radiomics and artificial intelligence in the context of head and neck tumor imaging given the exponential development of these technologies in medical imaging. These were geared at the creation of reliable and reproducible models based on quantitative data. Even if there are still a few obstacles to their widespread usage in clinical practice, it is clear that they have the potential to be revolutionary. In this paper, we provide a thorough overview of radiomics and artificial intelligence applications in head and neck tumor imaging. ABSTRACT: Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.