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title | Data Access |
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| Data AccessClick the Download button 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. Data Type | Download all or Query/Filter | License |
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Images and Radiation Therapy Structures (DICOM, 130GB) |
| Tissue Slide Images (web) | | Clinical Data (TXT) | | Genomics (web) | |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCGA-HNSC-08-30-2018-doiJNLP-PMK5AdS1.tcia?version=1&modificationDate=1541017261959&api=v2 |
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label | Search |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?CollectionCriteria=TCGA-HNSC |
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(Download requires the NBIA Data Retriever) | | Image RemovedClick the Versions tab for more info about data releases. Additional Resources for this DatasetThe NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data. Third Party Analyses of this DatasetTCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection: |
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title | Detailed Description |
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| Detailed Description | |
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Modalities computed tomography (CT) magnetic resonance (MR) positron emission tomography (PET) | CT, MR, PT, RTDOSE, RTPLAN, RTSTRUCT | Number of PatientsParticipants | 227 | Number of Studies | 479 | Number of Series | 2,561 | Number of Images | 270,376 | Images Size (GB) | 129.6 |
Total Cases by SiteSite | Patients |
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MDA (TSS code "-CV-") | 78 | UNC (TSS code "-BA-") | 22 | UPMC (TSS code "-CN-") | 5 | Vanderbilt (TSS code "-CR-") | 51 | JHU (TSS code "-BB-") | 15 | Miami (TSS code "-IQ-") | 5 | Barretos (TSS code "-UF-") | 16 | UHN (TSS code "-CQ-") | 35 | Total | 227 |
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Total Cases by Modality and Site Site | CT | MR | PT | Grand Total |
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JHU | 14 | 3 | 5 | 22 |
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MDA | 78 | 6 | 6 | 90 |
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UNC | 20 | 2 | 2 | 24 |
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UPMC | 5 | |
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Vanderbilt | 50 | 5 | 15 | 70 |
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Miami | 5 | | |
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Barretos | 16 | | |
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| 16 |
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UHN | 23 | 12 |
| 35 |
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Grand Total | 211 | 28 | 33 | 272 |
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GDC Data Portal - Clinical and Genomic DataThe 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: Case report forms for the clinical data collected by TCGA can be found on the Biospecimen Core Resource Clinical Data Forms linked below: 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 DatesSubject 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).
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net. Please be sure to include the following attribution and citations in your work if you use this data set: Info |
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| | "The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/." Tcia limited license policy |
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Info |
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| Zuley, M. L., Jarosz, R., Kirk, S., Lee, Y., Colen, R., Garcia, K., Delbeke, D., Pham, M., Nagy, P., Sevinc, G., Goldsmith, M., Khan, S., Net, J. M., Lucchesi, F. R., … & Aredes, N. D. (2016). Radiology Data from The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma [Collection (TCGA-HNSC] collection) (Version 6) [Data set]. The Cancer Imaging Archive. http https://doi.org/10.7937/K9/TCIA.2016.LXKQ47MS |
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| "The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/." |
Info |
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| 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 , Volume (Vol. 26, Number Issue 6, December, 2013, pp 1045-1057. (paper)1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7 |
Other Publications Using This DataTCIA maintainsmaintains a list of publications which leverage our data. - 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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).
- 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
. At this time we are not aware of any manuscripts based on this data. If you have a manuscript you'd like to add please contact the TCIA's Helpdesk.
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| Version 6 (Current): 2023/05/24Data Type | Download all or Query/Filter |
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Images and Radiation Therapy Structures (DICOM, 130GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCGA-HNSC-08-30-2018-doiJNLP-PMK5AdS1.tcia?version=1&modificationDate=1541017261959&api=v2 |
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label | Search |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?CollectionCriteria=TCGA-HNSC |
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(Download requires the NBIA Data Retriever) | Tissue Slide Images (web) |
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label | Search |
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url | https://portal.gdc.cancer.gov/image-viewer?filters=%7B%22op%22%3A%22and%22%2C%22content%22%3A%5B%7B%22content%22%3A%7B%22field%22%3A%22cases.case_id%22%2C%22value%22%3A%5B%22set_id%3AYEEmK4gB-Ny-1EyOwW07%22%5D%7D%2C%22op%22%3A%22IN%22%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22TCGA-HNSC%22%5D%7D%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22files.data_type%22%2C%22value%22%3A%5B%22Slide%20Image%22%5D%7D%7D%5D%7D |
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| Clinical Data (TXT) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/gdc_download_clinical_hnsc.tar.gz?version=1&modificationDate=1590772486422&api=v2 |
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| Biomedical Data (TXT) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/gdc_download_biomedical_hnsc.tar.gz?version=1&modificationDate=1590772470829&api=v2 |
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| Genomics (web) |
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label | Search |
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url | https://portal.gdc.cancer.gov/projects/TCGA-HNSC |
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Corrected modality for series: 1.3.6.1.4 (Current).1.14519.5.2.1.8421.4009.196228604563469888269110627731 . Version 5: 2020/05/29Data Type | Download all or Query/Filter |
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Images and Radiation Therapy Structures (DICOM, 130GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCGA-HNSC-08-30-2018-doiJNLP-PMK5AdS1.tcia?version=1&modificationDate=1541017261959&api=v2 |
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(Download requires the NBIA Data Retriever) | Tissue Slide Images (web) |
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label | Search |
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url | https://portal.gdc.cancer.gov/image-viewer?filters=%7B%22op%22%3A%22and%22%2C%22content%22%3A%5B%7B%22content%22%3A%7B%22field%22%3A%22cases.case_id%22%2C%22value%22%3A%5B%22set_id%3AYEEmK4gB-Ny-1EyOwW07%22%5D%7D%2C%22op%22%3A%22IN%22%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22TCGA-HNSC%22%5D%7D%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22files.data_type%22%2C%22value%22%3A%5B%22Slide%20Image%22%5D%7D%7D%5D%7D |
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| Clinical Data (TXT) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/gdc_download_clinical_hnsc.tar.gz?version=1&modificationDate=1590772486422&api=v2 |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/gdc_download_biomedical_hnsc.tar.gz?version=1&modificationDate=1590772470829&api=v2 |
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| Genomics (web) |
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label | Search |
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url | https://portal.gdc.cancer.gov/projects/TCGA-HNSC |
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Updated clinical data link with latest spreadsheets from GDC. Added new biomedical spreadsheets from GDC. Version 4: Updated 2018/08/30Data Type | Download all or Query/Filter |
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Images (DICOM, 130GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCGA-HNSC-08-30-2018-doiJNLP-PMK5AdS1.tcia?version=1&modificationDate=1541017261959&api=v2 |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCGA-HNSC%20Clinical%20Data%2008-30-18.zip?version=1&modificationDate=1535718756268&api=v2 |
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label | Search |
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url | https://portal.gdc.cancer.gov/projects/TCGA-HNSC |
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Added new DICOM images. Version 3: Updated 2017/03/30Data Type | Download all or Query/Filter |
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Images (DICOM, 108GB) | Image Removed Image Removed
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCGA-HNSC-v3-doiJNLP.tcia?version=1&modificationDate=1534786983237&api=v2 |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCGA-HNSC%20Clinical%20Data%201516.zip?version=1&modificationDate=1452101750759&api=v2 |
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label | Search |
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url | https://portal.gdc.cancer.gov/projects/TCGA-HNSC |
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Added new DICOM images. Version 2: Updated 2016/01/05Data Type | Download all or Query/Filter |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCIA_TCGA-HNSC-09-16-2015.tcia?version=1&modificationDate=1534787425505&api=v2 |
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCGA-HNSC%20Clinical%20Data%201516.zip?version=1&modificationDate=1452101750759&api=v2 |
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| Genomics (web) | Image Removed |
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label | Search |
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url | https://portal.gdc.cancer.gov/projects/TCGA-HNSC |
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Extracted latest release of clinical data (TXT) from the GDC Data Portal. Version 1: Updated 2014/11/26Data Type | Download all or Query/Filter |
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Images (DICOM, 88.1GB) | Image Removed Image Removed
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCIA_TCGA-HNSC-09-16-2015.tcia?version=1&modificationDate=1534787425505&api=v2 |
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(Download requires the NBIA Data Retriever) | Clinical Data (TXT) | Image Removed
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url | https://wiki.cancerimagingarchive.net/download/attachments/11829589/TCGA-HNSC%20Clinical%20Data.zip?version=1&modificationDate=1415210184908&api=v2 |
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