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

Aaron J. Grossberg, Abdallah S.R. Mohamed, Hesham El Halawani, William C. Bennett, Kirk E. Smith, Tracy S. Nolan, Bowman Williams, Sasikarn Chamchod, Jolien Heukelom, Michael E. Kantor, Theodora Browne, Katherine A. Hutcheson, G. Brandon Gunn, Adam S. Garden, William H. Morrison, Steven J. Frank, David I. Rosenthal, John B. Freymann, Clifton D. Fuller

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


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.

Original languageEnglish (US)
Article number180173
JournalScientific Data
StatePublished - 2018

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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