This study describes a subset of the HNSCC collection on TCIA.
There is an unmet need for integrating quantitative imaging biomarkers into current risk stratification tools and to explore the correlation between radiomics features –alone or in combination with clinical prognosticators- and tumor outcome. Clinical meta-data and matched baseline contrast-enhanced computed tomography (CECT) scans were used to build a cohort of 495 oropharyngeal cancer (OPC) patients treated between 2005 and 2012. Expert radiation oncologists manually segmented primary and nodal disease gross volumes (GTVp & GTVn). Structures were named per the American Association of Physicists in Medicine (AAPM) TG-263 recommendations, then retrieved in RT-STRUCT format. Matched patient, disease, treatment and outcomes data were obtained. Radiomics analysis was performed using an open-source institutionally-developed software that runs on Matlab platform.
A related dataset is here: Data from Head and Neck Cancer CT Atlas. DOI: 10.7937/K9/TCIA.2017.umz8dv6sand the combined collection is listed here: HNSCC
This research was supported by the Andrew Sabin Family Foundation; Dr. Fuller is a Sabin Family Foundation Fellow. Drs. Mohamed and Fuller receive funding support from the National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research (NIDCR) (R01DE025248) and the National Institutes of Health (NIH)/National Cancer Institute (NCI) (1R01CA214825-01).
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|Data Type||Download all or Query/Filter|
|Radiology Images and Radiation Therapy Structures (DICOM, 51.6 GB)|
|Clinical Data (CSV, 79kB)|
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Number of Patients
Number of Studies
Number of Series
Number of Images
|Images Size (GB)||51.6|
Diagnostic contrast-enhanced computed tomography (CECT) Digital Imaging and Communications in Medicine (DICOM) files prior to any active intervention were collected for 495 OPC patients treated at our institution between 2005 and 2012. Expert radiation oncologists manually segmented primary and nodal disease gross volumes (GTVp & GTVn). Structures were named per the American Association of Physicists in Medicine (AAPM) TG-263 recommendations, then retrieved in RT-STRUCT format. Matched patient, disease, treatment and outcomes data were obtained. Radiomics analysis was performed using an open-source institutionally-developed software that runs on Matlab platform. Links to these can be found in the related publication.
|title||Citations & Data Usage Policy|
Citations & Data Usage Policy These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to email@example.com. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:
|Public collection license|
Elhalawani H, White AL, Zafereo J, Wong AJ, Berends JE, AboHashem S, Williams B, Aymard JM, Kanwar A, Perni S, Mulder S, Rock CD, Grossberg A, Mohamed A, Gunn GB, Frank SJ, Rosenthal DI, Garden AS, Fuller CD; M.D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group (2018). Radiomics outcome prediction in Oropharyngeal cancer [Dataset]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.2vx6-fy46
Elhalawani, H., Mohamed, A., White, A. et al. Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. Sci Data 4, 170077 (2017). DOI: 10.1038/sdata.2017.77
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:
Other Publications Using This Data
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Version 1 (Current): 2020/03/31
|Data Type||Download all or Query/Filter|
|Images - 814 series (DICOM, 51.6 GB)|
|Clinical Data (CSV, 79 kB)|