Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer

Kareem A. Wahid; Brennan Olson; Rishab Jain; Aaron J. Grossberg; Dina El-Habashy; Cem Dede; Vivian Salama; Moamen Abobakr; Abdallah S.R. Mohamed; Renjie He; Joel Jaskari; Jaakko Sahlsten; Kimmo Kaski; Clifton D. Fuller; Mohamed A. Naser

doi:10.1038/s41597-022-01587-w

Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer

Kareem A. Wahid, Brennan Olson, Rishab Jain, Aaron J. Grossberg, Dina El-Habashy, Cem Dede, Vivian Salama, Moamen Abobakr, Abdallah S.R. Mohamed, Renjie He, Joel Jaskari, Jaakko Sahlsten, Kimmo Kaski, Clifton D. Fuller, Mohamed A. Naser

Knight Cancer Prevention and Control Program

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English (US)
Article number	470
Journal	Scientific Data
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41597-022-01587-w
State	Published - Dec 2022

ASJC Scopus subject areas

Statistics and Probability
Information Systems
Education
Computer Science Applications
Statistics, Probability and Uncertainty
Library and Information Sciences

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Wahid, K. A., Olson, B., Jain, R., Grossberg, A. J., El-Habashy, D., Dede, C., Salama, V., Abobakr, M., Mohamed, A. S. R., He, R., Jaskari, J., Sahlsten, J., Kaski, K., Fuller, C. D., & Naser, M. A. (2022). Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer. Scientific Data, 9(1), Article 470. https://doi.org/10.1038/s41597-022-01587-w

Wahid, KA, Olson, B, Jain, R, Grossberg, AJ, El-Habashy, D, Dede, C, Salama, V, Abobakr, M, Mohamed, ASR, He, R, Jaskari, J, Sahlsten, J, Kaski, K, Fuller, CD & Naser, MA 2022, 'Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer', Scientific Data, vol. 9, no. 1, 470. https://doi.org/10.1038/s41597-022-01587-w

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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.",

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Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer

Abstract

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