4.7 Article Data Paper

Data Descriptor: Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy

Journal

SCIENTIFIC DATA
Volume 5, Issue -, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/sdata.2018.173

Keywords

-

Funding

  1. Andrew Sabin Family Foundation
  2. National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research (NIDCR) [R01DE025248]
  3. National Institutes of Health (NIH)/National Cancer Institute (NCI) [1R01CA214825-01]
  4. NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Career Development Award [P50CA097007-10]
  5. NCI Paul Calabresi Clinical Oncology Program Award [K12 CA088084-06]
  6. General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award
  7. Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant
  8. Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center Seed Grant
  9. MD Anderson Institutional Research Grant (IRG) Program
  10. NIH/NCI Cancer Center Support (Core) Grant [CA016672]
  11. University of Texas MD Anderson Cancer Center [P30 CA016672]
  12. Family of Paul W. Beach

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Cross sectional imaging is essential for the patient -specific planning and delivery of radiotherapy, a primary determinant of head and neck cancer outcomes. Due to challenges ensuring data quality and patient de identification, publicly available datasets including diagnostic and radiation treatment planning imaging are scarce. In this data descriptor, we detail the collection and processing of computed tomography based imaging in 215 patients with head and neck squamous cell carcinoma that were treated with radiotherapy. Using cross sectional imaging, we calculated total body skeletal muscle and adipose content before and after treatment. We detail techniques for validating the high quality of these data and describe the processes of data de -identification and transfer. All imaging data are subject- and date -matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival. These data are a valuable resource for studying the association between patient-specific anatomic and metabolic features, treatment planning, and oncologic outcomes, and the first that allows for the integration of body composition as a risk factor or study outcome.

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