摘要：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 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, 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 . Subsequently, using publicly disseminated Python scripts, we generated corresponding segmentations fles 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. these data are a valuable resource for studying sarcopenia and body composition analysis in patients with HNC.
