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

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.

Keywords: artificial intelligence; diagnostic imaging; head and neck tumors; radiomics.

Figure 1

The “radiomic workflow” involves a series of iterative steps for reproducible and consistent extraction of imaging data. These steps include image acquisition, feature extraction and feature selection. This may be possible through deep-learning radiomics, handcrafted radiomics and delta radiomics. Finally, the selected features are used to test the final model.

Figure 2

The outline of our paper is shown in Figure 2. In particular, we describe the main steps of a radiomic workflow (segmentation and characterization) in order to obtain predictions of survival, metastasis and recurrence, and treatment responses.