ArticleScientific data2022
Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Improving the nutritional evaluation in head neck cancer patients using bioelectrical impedance analysis: Not only the phase angle matters.Journal of cachexia, sarcopenia and muscle · 2024Observational
- SAROS: A dataset for whole-body region and organ segmentation in CT imaging.Scientific data · 2024Article
- Nutrition in HNSCC: is it a matter for oncologists? The role of multidisciplinary team-a narrative literature review.Frontiers in oncology · 2024Review
- Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer.JAMA network open · 2023Article
- Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites.Scientific data · 2023Article
- Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.medRxiv : the preprint server for health sciences · 2023Article
- Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer.Frontiers in oncology · 2022Article
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Authors and funding
15 authors.
Funding
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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Registered trials
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