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title | Data Access |
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| Data AccessData Type | Download all or Query/Filter | License |
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Images and Segmentations (DICOM, 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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title | Detailed Description |
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| Detailed Description
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Modalities | PET, CT, SR, SEG, RWV | Number of Participants | 279 | Number of Studies | 1032 | Number of Series | 3837 | Number of Images | 701,002 | Image Size (GB) | 201.2 | - 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 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., Smith, B., Sunderland, 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. (2015). Data From QIN-HEADNECK (Version 4) [Data set]. The Cancer Imaging Archive. 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, 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 (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 (Vol. 26, Issue 6, pp. 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 TCIA data. If you have a manuscript you'd like to add please contact 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 GB) |
Tcia button generator |
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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, 104 GB) |
Tcia button generator |
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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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| 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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| 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, 102.76 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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| 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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