This collection has been deprecated. Data from the collection formerly called OPC-Radiomics has been updated. The data are downloadable but no longer viewable in the Cancer Imaging Archive. Please view the RADCURE page to obtain access to the updated data: https://doi.org/10.7937/J47W-NM11.
Oropharynx cancer is increasing in frequency in North America. More refined risk stratification and treatment personalization strategies are needed to improve cure rates and limit toxicities. Radomic features have shown promise for prognostication in oropharynx cancer based on datasets including hundreds of patients. Clinical outcomes data for oropharynx cancer patients treated at Princess Margaret Cancer Centre is of the highest quality, collected prospectively at the point of care. This data has resulted in the recent AJCC/UICC 8th edition staging system for oropharynx cancer (first published by O'Sullivan et al. in Lancet Oncology, 2016 [PMID 26936027]). By making publicly available the published data from our institution, which has now been published twice, we will empower researchers to perform meta-analysis and validation exercises of prognostic radiomic models. Ultimately this will lead to more robust models and bring us closer to clinical implementation and impact for our patients.
Distant metastasis (DM) is the main cause of death for patients with human papillomavirus (HPV)-related oropharyngeal cancers (OPC); yet there are few reliable predictors of DM in this disease. The role of quantitative imaging (i.e. radiomic) analysis was examined to determine whether there are primary tumor features discernible on imaging studies that associate with a higher risk of developing DM. This data collection consists of oropharynx squamous cell carcinoma.
Radiotherapy planning CT scans were retrieved for all non-metastatic p16-positive OPC patients treated with radiotherapy or chemoradiotherapy at a single institution between 2005 and 2010. Radiomic biomarkers were derived from each gross tumor volume (GTV). Biomarkers included four representative radiomic features from tumor first order statistics, shape, texture, and wavelet groups as well as a combined four-feature signature. Univariable Cox proportional hazards models for DM risk were identified. Discriminative performance of prognostic univariable and multivariable models was compared using the concordance index (C-index). Subgroup analyses were performed.
There were 300 HPV-related OPC patients who were eligible for the analysis. A total of 36 DM events occurred within a median follow-up of five years. On univariable analysis, top results included the four representative radiomic features (p<0.001), the radiomic signature (p<0.001), tumor stage (p<0.001), tumor diameter (p<0.001), and tumor volume (p<0.001). C-indices of the radiomic features (0.670-0.686), radiomic signature (0.670), stage (0.633), and tumor size metrics (0.653-0.674) demonstrated moderate discrimination of DM risk. Combined clinical-radiomic models yielded significantly improved performance (0.701-0.714; p<0.05). In subgroup analyses, the radiomic biomarkers consistently stratified patients for DM risk, particularly for those cohorts with greater risks (0.663-0.796), such as patients with stage III disease.
Radiomic biomarkers appear to classify DM risk for patients with non-metastatic HPV-related OPC. Radiomic biomarkers could be used either alone or with other clinical characteristics in assignment of DM risk in future HPV-related OPC clinical trials.
- 300 patients in this dataset are also a subset of 542 patients from Princess Margaret Cancer Centre published in 2015: External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. PMID: 26264429 DOI: 10.3109/0284186X.2015.1061214,
- This dataset is also described in Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma. PMID: 29506884 DOI: 10.1016/j.ijrobp.2018.01.057
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- Jennifer Yin Yee Kwan MD, Jie Su MSc, Shao Hui Huang MSc MRT(T) MD, Laleh S. Ghoraie PhD, Wei Xu PhD, Biu Chan, Kenneth W. Yip PhD, Meredith Giuliani MBBS MEd FRCPC, Andrew Bayley MD FRCPC, John Kim MD FRCPC, Andrew J. Hope MD FRCPC, Jolie Ringash MD MSc FRCPC, John Cho MD PhD FRCPC, Andrea McNiven PhD MCCPM, Aaron Hansen MBBS FRACP, David Goldstein MD FRCSC, John de Almeida MD MSc FRCSC, Hugo J. Aerts PhD, John N. Waldron MD MSc FRCPC, Benjamin Haibe-Kains PhD, Brian O’Sullivan MB BCh BAO FRCPC, Scott V. Bratman MD PhD FRCPC, Fei-Fei Liu MD FRCPC, Farnoosh Khodakarami, PhD, Katrina Rey-McIntyre, BSc, CCRP.
For scientific or other inquiries about this dataset, please contact TCIA's Helpdesk.
|Data Type||Download all or Query/Filter||License|
|Images and Radiation Therapy Structures (DICOM, 61.75 GB)|
(Download requires the NBIA Data Retriever)
Clinical (xlsx, 95 kB)
(e.g. patient demographics, outcomes)
Click the Versions tab for more info about data releases.
Number of Participants
Number of Studies
Number of Series
Number of Images
|Image Size (GB)||61.75|
- 300 patients in this dataset are also a subset of 542 patients from Princess Margaret Cancer Centre published in 2015: External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. PMID: 26264429 DOI: 10.3109/0284186X.2015.1061214 . You can download just this portion of the Collection using this link:
OPC-Radiomics-300.tcia 30.9 GB
to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.
- Please note:
For some cases, there are HTV RTSTRUCT information listed, without GTV contours. HTV refers to a preoperative GTV target region, for instance in the setting of tonsillectomy. If there exist both GTV and HTV for a case, then there is probably a postoperative residual tumor seen.There are three listed clinical outcome results (columns "Local Failure", "Regional Failure," Distant Failure"); "Persistent" would generally be considered treatment failure. Blank indicates no failure/recurrence in these columns.
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Kwan JYY, Su J, Huang SH, Ghoraie LS, Xu W, Chan B, Yip KW, Giuliani M, Bayley A, Kim J, Hope AJ, Ringash J, Cho J, McNiven A, Hansen A, Goldstein D, de Almeida JR, Aerts HJ, Waldron JN, Haibe-Kains B, O'Sullivan B, Bratman SV, Liu FF. (2019). Data from Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in Oropharyngeal Carcinoma. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.8dho2gls
Kwan JYY, Su J, Huang SH, Ghoraie LS, Xu W, Chan B, Yip KW, Giuliani M, Bayley A, Kim J, Hope AJ, Ringash J, Cho J, McNiven A, Hansen A, Goldstein D, de Almeida JR, Aerts HJ, Waldron JN, Haibe-Kains B, O'Sullivan B, Bratman SV, Liu FF. (2018) Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma . International Journal of Radiation Oncology*Biology*Physics, 1-10. https://doi.org/10.1016/j.ijrobp.2018.01.057
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. https://doi.org/10.1007/s10278-013-9622-7
Other Publications Using This Data
TCIA maintains a list of publications which leverage our data. If you have a publication you'd like to add please contact the TCIA Helpdesk. At this time the following related publication has been brought to TCIA's attention:
- Leijenaar RT, Carvalho S, Hoebers FJ, Aerts HJ, van Elmpt WJ, Huang SH, Chan B, Waldron JN, O'sullivan B, Lambin P. (2015) External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol. 54(9):1423-9. DOI: 10.3109/0284186X.2015.1061214
- Lombardo, E., Kurz, C., Marschner, S., Avanzo, M., Gagliardi, V., Fanetti, G., Franchin, G., Stancanello, J., Corradini, S., Niyazi, M., Belka, C., Parodi, K., Riboldi, M., & Landry, G. (2021). Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts. Scientific Reports, 11(1). DOI: 10.1038/s41598-021-85671-y
Version 1 (Current): Updated 2020/01/29
|Data Type||Download all or Query/Filter|
|Images (DICOM, 61.6 GB)|
(Download requires the NBIA Data Retriever)
|Summary clinical data|