Skeletal muscle and adipose tissue at the C3 vertebral level for TCIA HNSCC patients

The accurate determination of sarcopenia is critical for disease management in patients with head and neck cancer (HNC). Quantitative determination of sarcopenia is currently dependent on manually-generated segmentations of skeletal muscle derived from computed tomography (CT) cross-sectional imaging. This has prompted the increasing utilization of machine learning models for automated sarcopenia determination. However, extant datasets of head and neck CT imaging currently do not provide the necessary manually-generated skeletal muscle segmentations at the C3 vertebral level needed for building these models. In this data descriptor, we detail the annotation of a large set of head and neck CT images for use in automated sarcopenia-related clinical decision making and body composition analysis. A set of 394 HNC patients were selected from The Cancer Imaging Archive, and their skeletal muscle and adipose tissue was manually segmented at the C3 vertebral level using sliceOmatic in .tag format. Subsequently, using publicly disseminated Python scripts, we generated corresponding segmentations files in Neuroimaging Informatics Technology Initiative format. In addition to segmentation data, additional clinical demographic data germane to body composition analysis have been retrospectively collected for these patients from the MD Anderson databases. These data are a valuable resource for studying sarcopenia and body composition analysis in patients with HNC.<br>Corresponding data descriptor: https://www.nature.com/articles/s41597-022-01587-w.