Display options
Share it on

Sci Data. 2018 Sep 04;5:180173. doi: 10.1038/sdata.2018.173.

Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy.

Scientific data

Aaron J Grossberg, Abdallah S R Mohamed, Hesham Elhalawani, 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

Affiliations

  1. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
  2. Department of Radiation Medicine, Oregon Health and Science University, Portland, Oregon 97238, USA.
  3. Department of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, University of Alexandria, Alexandria, 21321 Egypt.
  4. Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205, USA.
  5. Radiation Oncology Unit, Chulabhorn Hospital, Bangkok 10210, Thailand.
  6. Department of Radiation Oncology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands.
  7. Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
  8. Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, Frederick, Maryland 20892, USA.

PMID: 30179230 PMCID: PMC6190723 DOI: 10.1038/sdata.2018.173

Abstract

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.

References

  1. Cancer. 1985 Jan 1;55(1 Suppl):238-49 - PubMed
  2. Eur J Cancer. 2015 Oct;51(15):2130-2143 - PubMed
  3. Radiographics. 2015 May-Jun;35(3):727-35 - PubMed
  4. J Digit Imaging. 2013 Dec;26(6):1045-57 - PubMed
  5. J Appl Physiol (1985). 1998 Jul;85(1):115-22 - PubMed
  6. Appl Physiol Nutr Metab. 2008 Oct;33(5):997-1006 - PubMed
  7. Lancet Oncol. 2008 Jul;9(7):629-35 - PubMed
  8. Int J Obes. 1986;10(1):53-67 - PubMed
  9. Cancer. 1996 May 1;77(9):1905-11 - PubMed
  10. Med Phys. 2010 Sep;37(9):4817-53 - PubMed
  11. Lancet Oncol. 2010 Jan;11(1):21-8 - PubMed
  12. J Digit Imaging. 2012 Feb;25(1):14-24 - PubMed
  13. CA Cancer J Clin. 2011 Mar-Apr;61(2):69-90 - PubMed
  14. N Engl J Med. 2010 Jul 1;363(1):24-35 - PubMed
  15. CA Cancer J Clin. 2016 Jan-Feb;66(1):7-30 - PubMed
  16. PLoS One. 2012;7(1):e29330 - PubMed
  17. J Appl Physiol (1985). 2004 Dec;97(6):2333-8 - PubMed
  18. Int J Radiat Oncol Biol Phys. 2016 Jul 1;95(3):895-904 - PubMed
  19. JAMA Oncol. 2016 Jun 1;2(6):782-9 - PubMed

MeSH terms

Publication Types

Grant support