@article{c96c52203c3b4e38b37c82fca5a814a3,
title = "Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer",
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.",
author = "Wahid, {Kareem A.} and Brennan Olson and Rishab Jain and Grossberg, {Aaron J.} and Dina El-Habashy and Cem Dede and Vivian Salama and Moamen Abobakr and Mohamed, {Abdallah S.R.} and Renjie He and Joel Jaskari and Jaakko Sahlsten and Kimmo Kaski and Fuller, {Clifton D.} and Naser, {Mohamed A.}",
note = "Funding Information: This work was supported by the National Institutes of Health (NIH)/National Cancer Institute (NCI) through a Cancer Center Support Grant (CCSG; P30CA016672–44). M.A.N. is supported by an NIH grant (R01DE028290–01). K.A.W. is supported by the Dr. John J. Kopchick Fellowship through The University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, the American Legion Auxiliary Fellowship in Cancer Research, and an NIH/National Institute for Dental and Craniofacial Research (NIDCR) F31 fellowship (1 F31DE031502–01). A.J.G. received funding from the National Cancer Institute (K08 245188, R01 CA264133) and the American Association for Cancer Research/Mark Foundation “Science of the Patient” Award (20–60–51-MARK). B.O. received funding from the National Cancer Institute (F30 CA254033) and Radiologic Society of North America Research Medical Student Grant (RMS2026). V.S. received funding from The University of Texas, Graduate School of Biomedical Sciences Graduate research assistantship. C.D.F. received funding from the NIH/NIDCR (1R01DE025248–01/R56DE025248); an NIH/NIDCR Academic-Industrial Partnership Award (R01DE028290); the National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679); the NIH Big Data to Knowledge (BD2K) Program of the NCI Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA214825); the NCI Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program (1R01CA218148); an NIH/NCI Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672); an NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50CA097007); and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Program (R25EB025787). Funding Information: C.D.F. has received direct industry grant support, speaking honoraria, and travel funding from Elekta AB. The other authors have no conflicts of interest to disclose. Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
month = aug,
day = "2",
doi = "10.1038/s41597-022-01587-w",
language = "English",
volume = "9",
pages = "1--6",
journal = "Scientific Data",
issn = "2052-4463",
publisher = "Nature Publishing Group",
number = "1",
}
