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title | Data Access |
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| Data AccessChoosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search By Collection option. Data Type | Download all or Query/Filter | License |
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Images and Segmentations (DICOM, | 104GB) | 201.2 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/6885289/QIN-HEADNECK%20NBIA-manifest.tcia_20200914.tcia?api=v2 |
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label | Search |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=QIN-HEADNECK |
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(Download requires NBIA Data Retriever) | | Clinical Data (.xlsx 68 kB) (See also Detailed Description) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/6885289/Batch_01%20and%20Batch_02%20Clinical%20Data_aug242020.xlsx?api=v2 |
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DICOM Metadata Digest (CSV) | | Clinical data | (See Detailed Description tab)
Click the Versions tab for more info about data releases. |
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title | Detailed Description |
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| Detailed Description
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Modalities | PET, CT, SR, SEG, RWV | Number of PatientsParticipants156 | 279 | Number of Studies651 | 1032 | Number of Series429 | 3837 | Number of Images353 | 701, | 408002 | Image Size (GB) | 104 | Access to this collection's clinical data is restricted due to the type of information included and/or the informed consent procedure under which the data were collected. If you believe this data will be useful for a current or planned research project, you may request access to this clinical data by completing the attached Data Use Agreement and forwarding it via e-mail to the TCIA help desk (help@cancerimagingarchive.net). The Data Use Agreement will be promptly reviewed by a TCIA review committee and you will be informed of their decision. In most cases access will be granted and members of your research team will be granted access to the clinical data. Note: you must have TCIA login credentials in order to access any restricted collection- Structured Report DICOM objects (Modality SR), are available for a subset of these subjects in the DICOM downloads and can be distinguished from image files by the series description "Clinical Data." Note, there is no image preview thumbnail for a Structured Report.
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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 citations in your work if you use this data set: Tcia limited license policy |
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Info |
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| Beichel, R. R., Ulrich, E. J., Bauer, C., Wahle, A., Brown, B., Chang, T., Plichta, K A., Smith, B J., Sunderland, J J., Braun, T., Fedorov, A., Clunie, D., Onken, M., Magnotta, V. A., Menda, Y., Riesmeier, J., Pieper, S., Kikinis, R., Graham, M.M., Casavant T. L., Sonka M,. & Buatti, J M. (2015). Data From QIN-HEADNECK (Version 4) [Data set]. The Cancer Imaging Archive. http https://doi.org/10.7937/K9/TCIA.2015.K0F5CGLI |
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title | Publication Citation |
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| Fedorov, A., Clunie, D., Ulrich, E., Bauer, C., Wahle, A., Brown, B., Onken, M., Riesmeier, J., Pieper, S., Kikinis, R., Buatti, J., & Beichel RR, R. R. (2016). DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. In PeerJ 4:e2057 (Vol. 4, p. e2057). PeerJ. https://doi.org/10.7717/peerj.2057 |
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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 PMCID: PMC3824915 |
Other Publications Using This DataTCIA maintains a list of publications which leverage our data, including citations of this CollectionTCIA data. If you have a publication manuscript you'd like to add please contact the TCIA's Helpdesk. - Ahmadvand, P., Duggan, N., Bénard, F., & Hamarneh, G. (2016). Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface. International Workshop on Machine Learning in Medical Imaging. doi: 10.1007/978-3-319-47157-0_33
- Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Clunie, D., Onken, M., Riesmeier, J., . . . Kikinis, R. (2020). Quantitative Imaging Informatics for Cancer Research. JCO Clin Cancer Inform, 4, 444-453. doi:https://doi.org/10.1200/CCI.19.00165
- Fedorov, A., Clunie, D., Ulrich, E., Bauer, C., Wahle, A., Brown, B., . . . Beichel, R. R. (2016). DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ, 4, e2057. doi: 10.7717/peerj.2057
- Ghattas, A. E. (2017). Medical Imaging Segmentation Assessment via Bayesian Approaches to Fusion, Accuracy and Variability Estimation with Application to Head and Neck Cancer. (PhD). The University of Iowa, Retrieved from http://ir.uiowa.edu/etd/5759
- Liang, X., Bassenne, M., Hristov, D. H., Islam, M. T., Zhao, W., Jia, M., . . . Xing, L. (2022). Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy. Comput Biol Med, 141, 105139. doi: 10.1016/j.compbiomed.2021.105139
- 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: 10.1016/j.cmpb.2023.107341
- Sinha, A. (2018). Deformable registration using shape statistics with applications in sinus surgery. (Ph. D.). Johns Hopkins University, Retrieved from http://jhir.library.jhu.edu/handle/1774.2/59202
- Sinha, A., Billings, S. D., Reiter, A., Liu, X., Ishii, M., Hager, G. D., & Taylor, R. H. (2019). The deformable most-likely-point paradigm. Medical image analysis, 55, 148-164. doi: 10.1016/j.media.2019.04.013
- Sinha et al. Towards automatic initialization of registration algorithms using simulated endoscopy images. link to article
- Sinha, A., Ishii, M., Hager, G. D., & Taylor, R. H. (2019). Endoscopic navigation in the clinic: registration in the absence of preoperative imaging. Int J Comput Assist Radiol Surg, 14(9), 1495-1506. doi: 10.1007/s11548-019-02005-0
- Smith, B. J., Buatti, J. M., Bauer, C., Ulrich, E. J., Ahmadvand, P., Budzevich, M. M., . . . Beichel, R. R. (2020). Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images. Tomography, 6(2), 65-76. doi: 10.18383/j.tom.2020.00004
- Stoll, M., Stoiber, E. M., Grimm, S., Debus, J., Bendl, R., & Giske, K. (2016). Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations. PLoS One, 11(12), e0168916. doi: 10.1371/journal.pone.0168916
- Taghanaki, S. A., Duggan, N., Ma, H., Hou, X., Celler, A., Benard, F., & Hamarneh, G. (2018). Segmentation-free direct tumor volume and metabolic activity estimation from PET scans. Comput Med Imaging Graph, 63, 52-66. doi: 10.1016/j.compmedimag.2017.12.004
- 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). doi: 10.1007/978-981-16-3880-0_27
- 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
- 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: 10.1002/mp.14320
- 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: 10.1371/journal.pone.0236841
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Localtab |
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| Version 4 (Current) : Updated 2020/09/15 Data Type | Download all or Query/Filter |
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Images and Segmentations (DICOM 201.2 | (Current)GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/6885289/QIN-HEADNECK%20NBIA-manifest.tcia_20200914.tcia?api=v2 |
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label | Search |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=QIN-HEADNECK |
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(Download requires the NBIA Data Retriever) | Clinical Data (.xlsx 68 kB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/6885289/Batch_01%20and%20Batch_02%20Clinical%20Data_aug242020.xlsx?api=v2 |
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Added 123 new subjects (Patient IDs = QIN-HeadNeck-02-####). Added missing PT or CT pre-treatment and follow up scans to 28 of the previously existing QIN-HeadNeck-01-#### subjects. Added supporting clinical data in XLSX format for all patients. Version 3: Updated 2019/07/24 Data Type | Download all or Query/Filter |
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Images (DICOM, 103.5 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/6885289/TCIA_QIN-HEADNECK_2019-07-24.tcia?version=3&modificationDate=1563997513437&api=v2 |
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| DICOM Metadata Digest (CSV) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/6885289/QIN-HEADNECK_TCIAmanifest_metadata_Jul2019.csv?version=2&modificationDate=1563997454543&api=v2 |
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| Lifted restriction from SR object data download. Version 2: Updated 2017/12/06Downloads require the NBIA Data Retriever . Data Type | Download all or Query/Filter |
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Images (DICOM, 104GB104 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/6885289/QIN-HEADNECK-12-06-2017-doiJNLP-Hb7bP6pf.tcia?version=1&modificationDate=1534786972624&api=v2 |
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Image Removed Image Removed (Requires TCIA Downloader App)
| DICOM Metadata Digest (CSV) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/6885289/QIN-HEADNECK_MetaData.csv?version=1&modificationDate=1495728510607&api=v2 |
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| Image Removed
| Added associated DICOM SEG, SR, and RWV objects Version 1: Updated 2015/08/20 Data Type | Download all or Query/Filter |
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Images (DICOM, | 10327GB)Image Removed 76 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/6885289/TCIA_QIN-HEADNECK_06-22-2015.tcia?version=1&modificationDate=1534787423317&api=v2 |
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| (Requires TCIA Downloader App) | DICOM Metadata Digest (CSV) |
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