Faculty, Staff and Student Publications
Publication Date
8-2-2022
Journal
Scientific Data
Abstract
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 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. These data are a valuable resource for studying sarcopenia and body composition analysis in patients with HNC.
Keywords
Adipose Tissue, Head and Neck Neoplasms, Humans, Muscle, Skeletal, Retrospective Studies, Sarcopenia, Skeletal muscle, Prognostic markers
DOI
10.1038/s41597-022-01587-w
PMID
35918336
PMCID
PMC9346108
PubMedCentral® Posted Date
8-2-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons, Otolaryngology Commons, Otorhinolaryngologic Diseases Commons