Head-Neck-PET-CT | Head-Neck-PET-CT
DOI: 10.7937/K9/TCIA.2017.8oje5q00 | Data Citation Required | 3.8k Views | 47 Citations | Image Collection
| Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated | |
|---|---|---|---|---|---|---|---|---|
| Head-Neck | Human | 298 | Other, REG, RTSTRUCT, CT, RTDOSE, RTPLAN, PT, Demographic, Diagnosis, Follow-Up, Treatment | Head and Neck Cancer | Clinical, Image Analyses, Software/Source Code | Limited, Complete | 2018/06/07 |
Summary
This collection contains FDG-PET/CT and radiotherapy planning CT imaging data of 298 patients from four different institutions in Québec with histologically proven head-and-neck cancer (H&N) All patients had pre-treatment FDG-PET/CT scans between April 2006 and November 2014, and within a median of 18 days (range: 6-66) before treatment. Dates in the TCIA images have been changed in the interest of de-identification; the same change was applied across all images, preserving the time intervals between serial scans. These patients were all part of a study described in further detail (treatment, image scanning protocols, etc.) in the publication: Note: Subsequent to publishing this manuscript it was discovered images from two patients included in the analysis had errors and should not be used in future studies. Therefore these have not been included in this TCIA data set, leaving 298 patients of the original 300 analyzed. In the original study, 93 of the 300 patients (31 %), the radiotherapy contours were directly drawn on the CT of the FDG-PET/CT scan by expert radiation oncologists and thereafter used for treatment planning. For 207 of the 300 patients (69 %), the radiotherapy contours were drawn on a different CT scan dedicated to treatment planning and were propagated/resampled to the FDG-PET/CT scan reference frame using intensity-based free-form deformable registration with the software MIM® (MIM software Inc., Cleveland, OH). We analyzed the FDG-PET and CT images of the 300 patients from four different cohorts for the risk assessment of locoregional recurrences (LR) and distant metastases in H&N cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups.
Patients with recurrent H&N cancer or with metastases at presentation, and patients receiving palliative treatment were excluded from the study. From the 300 patients, 48 received radiation alone (16 %) and 252 received chemo-radiation (84 %) with curative intent as part of treatment management. The median follow-up period of all patients was 43 months (range: 6-112). Patients that did not develop a locoregional recurrence or distant metastases during the follow-up period and that had a follow-up time smaller than 24 months were also excluded from the study. During the follow-up period, 45 patients developed a locoregional recurrence (15 %), 40 patients developed distant metastases (13 %) and 56 patients died (19 %).
Data Access
Some data in this collection contains images that could potentially be used to reconstruct a human face. Due to NIH Controlled Data Access Policy changes, downloads that previously required login-access are no longer available via TCIA. Watch for announcements re-enabling these in a new form.
Version 2: Updated 2018/06/07
Added 250 total DICOM series to 162 total subjects that had been missing.
| Title | Data Type | Format | Access Points | Subjects | License | Metadata | |||
|---|---|---|---|---|---|---|---|---|---|
| Images and Radiation Therapy Structures | REG, RTSTRUCT, CT, RTDOSE, RTPLAN, PT | DICOM | 298 | 504 | 2,661 | 123,271 | NIH Controlled Data Access Policy | View | |
| Clinical Data | Demographic, Diagnosis, Follow-Up, Treatment | XLSX | 300 | CC BY 3.0 | — | ||||
| Names of GTV contours | Other | XLSX | 298 | CC BY 3.0 | — |
Additional Resources for this Dataset
The following external resources have been made available by the data submitters. These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.
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 |
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Martin Vallières, Emily Kay-Rivest, Léo Jean Perrin, Xavier Liem, Christophe Furstoss, Nader Khaouam, Phuc Félix Nguyen-Tan, Chang-Shu Wang, Khalil Sultanem. (2017). Data from Head-Neck-PET-CT. The Cancer Imaging Archive. doi: https://doi.org/10.7937/K9/TCIA.2017.8oje5q00 |
Detailed Description
We hope the available data and source code will facilitate the standardization and reproducibility of methods in the radiomics community.
- Clinical Data – This spreadsheet includes patient information, histopathological type, tumour grade, outcome follow-up information (metastases, survival), etc.
- Names of GTV contours — This spreadsheet contains all the names of the “GTV primary” and “GTV lymph nodes” structures (as found in the associated RTstruct files) used in the publication of (Vallières et al., Sci Rep 7, 2017). Names of different structures are separated by commas in a given entry of the spreadsheet.
- Source Code – All software code implemented in this work is freely shared under the GNU General Public License at: https://github.com/mvallieres/radiomics .
Note: the images contain no private-vendor DICOM tags.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- McGill University, Montreal, Canada - Special thanks to Martin Vallières of the Medical Physics Unit
Related Publications
Publications by the Dataset Authors
The authors recommended the following as the best source of additional information about this dataset:
Publication Citation |
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Vallières, M., Kay-Rivest, E., Perrin, L. J., Liem, X., Furstoss, C., Aerts, H. J. W. L., Khaouam, N., Nguyen-Tan, P. F., Wang, C.-S., Sultanem, K., Seuntjens, J., & El Naqa, I. (2017). Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. In Scientific Reports (Vol. 7, Issue 1). DOI: https://doi.org/10.1038/s41598-017-10371-5 |
No other publications were recommended by dataset authors.
Research Community Publications
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