Skip to main content

Oropharyngeal-Radiomics-Outcomes

Oropharyngeal-Radiomics-Outcomes | Radiomics outcome prediction in Oropharyngeal cancer

DOI: 10.7937/TCIA.2020.2vx6-fy46 | Data Citation Required | Analysis Result

Cancer Types Location Subjects Related Collections Size Supporting Data Updated
Oropharyngeal Head-Neck 412 55.11GB Clinical, radiomic features 2022/08/02

Summary

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.umz8dv6s

Data Access

Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to help@cancerimagingarchive.net before accessing the data.

Version 2: Updated 2022/08/02

Corrected version of the clinical data CSV attached, because the investigators noticed an error in some of the durations of the endpoints including the overall survival, local and regional control, and freedom from distant metastasis. The original excel sheet had errors because the formulas to calculate the duration for patients with events were not applied so we fixed this error and now all the durations are correct.

Title Data Type Format Access Points Subjects Studies Series Images License
Radiomics outcome prediction in Oropharyngeal cancer Images RTSTRUCT, CT DICOM
Download requires NBIA Data Retriever
412 412 814 104,558 TCIA Restricted
Radiomics outcome prediction in Oropharyngeal cancer Clinical Data CSV CC BY 4.0

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

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

Detailed Description

Methods

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.

Note from the investigators: Some PET scans will include two PET AC files—one includes the head & neck portion of the exam, the other includes eyes-to-thighs. There is no file naming convention to distinguish between the two, so delineation may require the use of a DICOM viewer.

Acknowledgements

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).

Dr. Fuller received/(s) grant and/or salary support from the NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Career Development Award (P50CA097007-10); the NCI Paul Calabresi Clinical Oncology Program Award (K12 CA088084-06); a General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award;  an Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant; the Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center Seed Grant; the MD Anderson Institutional Research Grant (IRG) Program; and the NIH/NCI Cancer Center Support (Core) Grant CA016672 to The University of Texas MD Anderson Cancer Center (P30 CA016672).

Dr. Elhalawani was directly funded in part by a philanthropic gift from the Family of Paul W. Beach given to Dr. Gunn for patient-outcome database construction.

Publications Using This Data

TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Publication Citation

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

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Previous Versions

Version 1: Updated 2020/03/31

Title Data Type Format Access Points Subjects Studies Series Images License
Images - DICOM 814
Clinical Data CSV

Collections Used In This Analysis Result

Related Collections