The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma Collection (TCGA-HNSC)

Detailed Description

Total Cases by Site

Total Cases by Modality and Site

GDC Data Portal - Clinical and Genomic Data

The Genomic Data Commons (GDC) Data Portal has extensive clinical and genomic data, which can be matched to the patient identifiers on the images here in TCIA.  Below is a snapshot of clinical data extracted on 1/5/2016:

• TCGA-HNSC Clinical Data.zip

Case report forms for the  clinical data collected by TCGA can be found on the Biospecimen Core Resource Clinical Data Forms linked below:

• Head and Neck Case Quality Control Form
• Head and Neck Enrollment Form
• Head and Neck Follow-Up Form

The contents of the clinical_patient file as of June 29, 2014, for cases whose imaging are archived on TCIA are here.

A Note about TCIA and TCGA Subject Identifiers and Dates

Subject Identifiers: a subject with radiology images stored in TCIA is identified with a Patient ID that is identical to the Patient ID of the same subject with demographic, clinical, pathological, and/or genomic data stored in TCGA. For each TCGA case, the baseline TCGA imaging studies found on TCIA are pre-surgical. 

Dates: TCIA and TCGA handle dates differently, and there are no immediate plans to reconcile:

• TCIA Dates: dates (be they birth dates, imaging study dates, etc.) in the Digital Imaging and Communications in Medicine (DICOM) headers of TCIA radiology images have been offset by a random number of days. The offset is a number of days between 3 and 10 years prior to the real date that is consistent for each TCIA image-submitting site and collection, but that varies among sites and among collections from the same site. Thus, the number of days between a subject’s longitudinal imaging studies are accurately preserved when more than one study has been archived while still meeting HIPAA requirements.
• TCGA Dates: the patient demographic and clinical event dates are all the number of days from the index date, which is the actual date of pathologic diagnosis. So all the dates in the data are relative negative or positive integers, except for the “days_to_pathologic_diagnosis” value, which is 0 – the index date. The years of birth and diagnosis are maintained in the distributed clinical data file. The NCI retains a copy of the data with complete dates, but those data are not made available.With regard to other TCGA dates, if a date comes from a HIPAA “covered entity’s” medical record, it is turned into the relative day count from the index date. Dates like the date TCGA received the specimen or when the TCGA case report form was filled out are not such covered dates, and they will appear as real dates (month, day, and year).

Citations & Data Usage Policy 

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

Other Publications Using This Data

TCIA maintains a list of publications which leverage our data. 

1. Babier, A., Zhang, B., Mahmood, R., Moore, K. L., Purdie, T. G., McNiven, A. L., & Chan, T. C. Y. (2021). OpenKBP: The open‐access knowledge‐based planning grand challenge and dataset. Medical Physics. doi:10.1002/mp.14845
2. Dalvit Carvalho da Silva, R. (2022). The Role of Transient Vibration of the Skull on Concussion. (Ph. D. ). University of Western Ontario, Retrieved from https://ir.lib.uwo.ca/etd/8399 
3. Dalvit Carvalho da Silva, R., Jenkyn, T. R., & Carranza, V. A. (2021). Development of a Convolutional Neural Network Based Skull Segmentation in MRI Using Standard Tesselation Language Models. Journal of Personalized Medicine, 11(4), 310. doi:https://doi.org/10.3390/jpm11040310 
4. Dalvit Carvalho da Silva, R., Jenkyn, T. R., & Carranza, V. A. (2021). Development of a Convolutional Neural Network Based Skull Segmentation in MRI Using Standard Tesselation Language Models. J Pers Med, 11(4). doi:10.3390/jpm11040310
5. Hou, Y. X., Ren, Z., Tao, Y. B., & Chen, W. (2021). Learning-based parameter prediction for quality control in three-dimensional medical image compression. [基于学习方法的三维医学图像压缩质量控制参数预测]. Frontiers of Information Technology & Electronic Engineering, 22(9), 1169-1178. doi:10.1631/FITEE.2000234
6. Huang, C., Cintra, M., Brennan, K., Zhou, M., Colevas, A. D., Fischbein, N., . . . Gevaert, O. (2019). Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes. EBioMedicine, 45, 70-80. doi:10.1016/j.ebiom.2019.06.034
7. Kann, B. H., Hicks, D. F., Payabvash, S., Mahajan, A., Du, J., Gupta, V., . . . Aneja, S. (2020). Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma. J Clin Oncol, 38(12), 1304-1311. doi:10.1200/JCO.19.02031
8. Katsoulakis, E., Yu, Y., Apte, A. P., Leeman, J. E., Katabi, N., Morris, L., . . . Oh, J. H. (2020). Radiomic analysis identifies tumor subtypes associated with distinct molecular and microenvironmental factors in head and neck squamous cell carcinoma. Oral Oncology, 110, 104877. doi:https://doi.org/10.1016/j.oraloncology.2020.104877
9. Lv, W., Zhou, Z., Peng, J., Peng, L., Lin, G., Wu, H., . . . Lu, L. (2023). Functional-structural Sub-region Graph Convolutional Network (FSGCN): Application to the Prognosis of Head and Neck Cancer with PET/CT imaging. Computer Methods and Programs in Biomedicine. doi:https://doi.org/10.1016/j.cmpb.2023.107341
10. Mukherjee, P., Cintra, M., Huang, C., Zhou, M., Zhu, S., Colevas, A. D., . . . Gevaert, O. (2020). CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma. Radiol Imaging Cancer, 2(3), e190039. doi:https://doi.org/10.1148/rycan.2020190039
11. Na, K. J., & Choi, H. (2018). Tumor Metabolic Features Identified by (18)F-FDG PET Correlate with Gene Networks of Immune Cell Microenvironment in Head and Neck Cancer. Journal of Nuclear Medicine, 59(1), 31-37. doi:10.2967/jnumed.117.194217
12. Nikolov, S., Blackwell, S., Zverovitch, A., Mendes, R., Livne, M., De Fauw, J., . . . Ronneberger, O. (2021). Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study. J Med Internet Res, 23(7), e26151. doi:10.2196/26151
13. Singh, A., Goyal, S., Rao, Y. J., & Loew, M. (2019). A Novel Imaging-Genomic Approach to Predict Outcomes of Radiation Therapy. (MS). George Washington University, https://scholarspace.library.gwu.edu/etd/kh04dq40j
14. Thomas, R., Schalck, E., Fourure, D., Bonnefoy, A., & Cervera-Marzal, I. (2021). 2Be3-Net: Combining 2D and 3D Convolutional Neural Networks for 3D PET Scans Predictions. Paper presented at the International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). 
15. Vrtovec, T., Močnik, D., Strojan, P., Pernuš, F., & Ibragimov, B. (2020). Auto‐segmentation of organs at risk for head and neck radiotherapy planning: from atlas‐based to deep learning methods. Medical Physics, 47, e929-e950. doi: https://doi.org/10.1002/mp.14320
    Wong, J., Fong, A., McVicar, N., Smith, S., Giambattista, J., Wells, D., . . . Alexander, A. (2019). Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol, 144, 152-158. doi:10.1016/j.radonc.2019.10.019

If you have a manuscript you'd like to add please contact TCIA's Helpdesk.

Version 6 (Current): 2023/05/24

Corrected modality for series: 1.3.6.1.4.1.14519.5.2.1.8421.4009.196228604563469888269110627731 . 

Version 5: 2020/05/29

Updated clinical data link with latest spreadsheets from GDC. Added new biomedical spreadsheets from GDC.

Version 4: Updated 2018/08/30

Added new DICOM images.

Version 3: Updated 2017/03/30

Added new DICOM images.

Version 2: Updated 2016/01/05

Extracted latest release of clinical data (TXT) from the GDC Data Portal.

Version 1: Updated 2014/11/26