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DOI: 10.7937/k9/tcia.2020.a8sh-7363 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Head-Neck Human 627 CT, PT, MR, RTSTRUCT, RTPLAN, RTDOSE Head and Neck Squamous Cell Carcinoma 509.43GB Clinical, Image Analyses Limited, Complete 2022/08/24


This collection contains imaging, radiation therapy, and clinical data from 627 head and neck squamous cell carcinoma (HNSCC) patients at MD Anderson Cancer Center.  Researchers at MDACC analyzed these patients' data as part of two separate research projects.  70 of the patients were selected for inclusion in both projects. This collection provides access to all data for the full set of patients involved in both projects.  Download options for the individual studies are available by following the links in each project description below.

The first project: Head-Neck-CT-Atlas

The first project screened 2840 consecutive patients with HNSCC treated with curative-intent RT at MD Anderson Cancer Center from 2003 to 2013. Patients with whole-body PET-CT or abdominal CT scans both before and after RT (n=215) were selected for the cohort.  De-identified diagnostic imaging, radiation treatment planning, and follow up imaging are provided. Using cross sectional imaging, total body skeletal muscle and adipose content were calculated before and after treatment.  All imaging data are subject- and date-matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival. Open access to these data allows for inter-institutional comparisons of complete RT details in non-randomized patient populations, allowing for a more granular understanding of three dimensional factors that influence treatment effectiveness and toxicity sparing.  More information about this study and links to download the corresponding patient subset of this collection including clinical data can be found in the "Head-Neck-CT-Atlas" dataset.

The second project: Radiomics outcome prediction in Oropharyngeal cancer

The second project was intended to address the 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.  More information about this study and links to download the corresponding patient subset of this collection including clinical data can be found in the "Radiomics outcome prediction in Oropharyngeal cancer" dataset.

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 before accessing the data.

Version 3: Updated 2022/08/24

Corrected version of the Clinical Data from Radiomics outcome prediction in Oropharyngeal cancer” (XLS) 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
Images and Radiation Therapy Structures MR, RTPLAN, RTDOSE, RTSTRUCT, CT, PT DICOM
Download requires NBIA Data Retriever
627 1,177 4,039 537,942 TCIA Restricted
Head-Neck-CT-Atlas Images and Radiation Therapy Structures MR, RTPLAN, RTDOSE, RTSTRUCT, CT, PT DICOM
Download requires NBIA Data Retriever
215 765 3,225 433,384 TCIA Restricted
Head-Neck-CT-Atlas Clinical Data XLS CC BY 3.0
Head-Neck-CT-Atlas Data Dictionary XLS CC BY 3.0
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

Grossberg A, Elhalawani H, Mohamed A, Mulder S, Williams B, White AL, Zafereo J, Wong AJ, Berends JE, AboHashem S, Aymard JM, Kanwar A, Perni S, Rock CD, Chamchod S, Kantor M, Browne T, Hutcheson K, Gunn GB, Frank SJ, Rosenthal DI, Garden AS, Fuller CD, M.D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group. (2020) HNSCC [ Dataset ]. The Cancer Imaging Archive. DOI:

Detailed Description

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.


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.

Other 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.

  1. Cai, C., Lv, W., Chi, F., Zhang, B., Zhu, L., Yang, G., . . . Lu, L. (2022). Prognostic generalization of multi-level CT-dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma. Med Phys. doi:
  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 database. (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:
  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. Gouthamchand, V., Choudhury, A., Hoebers, F., Wesseling, F., Welch, M., Kim, S., . . . Wee, L. (2023). Privacy-Preserving Dashboard for F.A.I.R Head and Neck Cancer data supporting multi-centered collaborations. Paper presented at the 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2023), Basel, Switzerland.
  6. Jonsson, H., Ekstrom, S., Strand, R., Pedersen, M. A., Molin, D., Ahlstrom, H., & Kullberg, J. (2022). An image registration method for voxel-wise analysis of whole-body oncological PET-CT. Sci Rep, 12(1), 18768. doi:
  7. Kazmierski, M., Welch, M., Kim, S., McIntosh, C., Rey-McIntyre, K., Huang, S. H., . . . Haibe-Kains, B. (2023). Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics. Cancer Research Communications, 3(6), 1140-1151. doi:10.1158/2767-9764.Crc-22-0152
  8. La Greca Saint-Esteven, A., Bogowicz, M., Konukoglu, E., Riesterer, O., Balermpas, P., Guckenberger, M., . . . van Timmeren, J. E. (2022). A 2.5D convolutional neural network for HPV prediction in advanced oropharyngeal cancer. Comput Biol Med, 142, 105215. doi:10.1016/j.compbiomed.2022.105215
  9. Le, Q. C., Arimura, H., Ninomiya, K., & Kabata, Y. (2020). Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients. Sci Rep, 10(1), 1-12. doi:10.1038/s41598-020-78338-7
  10. Lv, W., Xu, H., Han, X., Zhang, H., Ma, J., Rahmim, A., & Lu, L. (2022). Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer. Cancers (Basel), 14(7). doi:
  11. Sasaki, A., Hu, N., Matsubayashi, N., Takata, T., Sakurai, Y., Suzuki, M., & Tanaka, H. (2023). Development of optimization method for uniform dose distribution on superficial tumor in an accelerator-based boron neutron capture therapy system. J Radiat Res. doi:
  12. Sasaki, A., Hu, N., Takata, T., Matsubayashi, N., Sakurai, Y., Suzuki, M., & Tanaka, H. (2022). Intensity-modulated irradiation for superficial tumors by overlapping irradiation fields using intensity modulators in accelerator-based BNCT. Journal of Radiation Research, 1-8. doi:10.1093/jrr/rrac052
  13. Shiri, I., Razeghi, B., Vafaei Sadr, A., Amini, M., Salimi, Y., Ferdowsi, S., . . . Zaidi, H. (2023). Multi-institutional PET/CT image segmentation using federated deep transformer learning. Comput Methods Programs Biomed, 240, 107706. doi:
  14. Shiri, I., Vafaei Sadr, A., Akhavan, A., Salimi, Y., Sanaat, A., Amini, M., . . . Zaidi, H. (2023). Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning. European Journal of Nuclear Medicine and Molecular Imaging, 50(4), 1034-1050. doi:10.1007/s00259-022-06053-8
  15. Sund Aillet, A. (2023). Self-supervised pre-training of an attention-based model for 3D medical image segmentation. (M.S. Thesis). KTH Royal Institute of Technology, Retrieved from database.
  16. Tang, F. H., Cheung, E. Y., Wong, H. L., Yuen, C. M., Yu, M. H., & Ho, P. C. (2022). Radiomics from Various Tumour Volume Sizes for Prognosis Prediction of Head and Neck Squamous Cell Carcinoma: A Voted Ensemble Machine Learning Approach. Life (Basel), 12(9). doi:
  17. 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).
  18. Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T. N., Apfaltrer, P., Tareco Bucho, T. M., . . . Beets-Tan, R. G. H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol, 11, 637804. doi:10.3389/fonc.2021.637804
  19. Wahid, K. A., Olson, B., Jain, R., Grossberg, A. J., El-Habashy, D., Dede, C., . . . Naser, M. A. (2022). Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer. Sci Data, 9(1), 470. doi:10.1038/s41597-022-01587-w
  20. Ye, Z., Saraf, A., Ravipati, Y., Hoebers, F., Catalano, P. J., Zha, Y., . . . Kann, B. H. (2023). Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer. JAMA Network Open, 6(8), e2328280-e2328280. doi:10.1001/jamanetworkopen.2023.28280
  21. Zschaeck, S., Li, Y., Lin, Q., Beck, M., Amthauer, H., Bauersachs, L., . . . Hofheinz, F. (2020). Prognostic value of baseline [18F]-fluorodeoxyglucose positron emission tomography parameters MTV, TLG and asphericity in an international multicenter cohort of nasopharyngeal carcinoma patients. PLoS One, 15(7), e0236841. doi:

Publication Citation

Grossberg  A, Mohamed A, Elhalawani H, Bennett W, Smith K, Nolan T,  Williams B, Chamchod S, Heukelom J, Kantor M, Browne T, Hutcheson K, Gunn G, Garden A, Morrison W, Frank S, Rosenthal D, Freymann J, Fuller C. (2018) Imaging and Clinical Data Archive for Head and Neck Squamous Cell Carcinoma Patients Treated with Radiotherapy. Scientific Data 5:180173 (2018) DOI: 10.1038/sdata.2018.173

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.

Previous Versions

Version 2: Updated 2020/03/31

Added data from the “Radiomics outcome prediction in Oropharyngeal cancer” project.

Title Data Type Format Access Points Studies Series Images License
Images and Radiation Therapy Structures DICOM
Clinical Data from Data from Head and Neck Cancer CT Atlas XLS
Clinical Data Dictionary from Data from Head and Neck Cancer CT Atlas XLS
Clinical Data from Radiomics outcome prediction in Oropharyngeal cancer XLS

Version 1: Updated 2019/07/11

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Clinical XLS
Data Dictionary XLS
Analysis Results Using This Collection