Quantitative measurements of masseter fat infiltration in head and neck cancer using Dixon conjugated with machine learning auto-segmentation

Motivation: Pathological changes in the masseter muscle have been associated with head and neck cancer (HNC). Nevertheless, investigations on the quantification of fatty infiltration in the masseter muscle and its correlation with HNC is limited. Goal(s): We aim to assess fatty infiltration, morphological characteristics, and texture features of the masseter muscle in HNC. Approach: This study sought to employ the Dixon method for fat fraction estimation conjugated with a machine learning-based auto-segmentation of the masseter muscle. Results: Our analysis revealed an elevated level of fatty infiltration in the masseter muscle among patients with head and neck cancer. Impact: Dixon method conjugated with machine learning-based auto-segmentation should facilitate in reliably assessing masseter fat alteration in head and neck cancer (HNC), this may be beneficial in response prediction in HNC treatment.