Skip to main content

QIN-HEADNECK

The Cancer Imaging Archive

QIN-HEADNECK | QIN-HEADNECK

DOI: 10.7937/K9/TCIA.2015.K0F5CGLI | Data Citation Required | 284 Views | 22 Citations | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Head-Neck Human 279 Demographic, Exposure, Treatment, Diagnosis, Follow-Up, SR, RWV, SEG, CT, PT Head and Neck Carcinomas 201.25GB Clinical, Image Analyses Limited, Complete 2020/09/15

Summary

This collection is a set of head and neck cancer patients' multiple positron emission tomography/computed tomography (PET/CT) 18F-FDG scans–before and after therapy–with follow up scans where clinically indicated. The data was provided to help facilitate research activities of the National Cancer Institute's (NCI's) Quantitative Imaging Network (QIN). This collection was supported by Grants: U24 CA180918 (http://qiicr.org) and U01 CA140206.

The following schematic summarizes much of the work done within the QIICR grant to augment the PET/CT scans with segmentations and clinical data using the DICOM standard: (click to enlarge)

About the NCI QIN

The mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.

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 help@cancerimagingarchive.net before accessing the data.

Version 4: Updated 2020/09/15

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.

Title Data Type Format Access Points Subjects Studies Series Images License
Images and Segmentations SR, RWV, SEG, CT, PT DICOM
Download requires NBIA Data Retriever
 279 1,032 3,837 701,002 TCIA Restricted
Clinical Data (See also Detailed Description) Demographic, Exposure, Treatment, Diagnosis, Follow-Up XLSX CC BY 3.0
Analysis Results Using This Collection
Related Datasets
SAROS
 No related Collections found
Legend: Analysis Results| Collections

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

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

Detailed Description

Associated Clinical Metadata

  • 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.

Related Publications

Publications by the Dataset Authors

The authors recommended the following as the best source of additional information about this dataset:

Publication Citation

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

No other publications were recommended by dataset authors.

Publication Citation

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

Research Community Publications

TCIA 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

TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Other Publications Using this Data

TCIA 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

Previous Versions

Version 3: Updated 2019/07/24

Lifted restriction from SR object data download.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
DICOM Metadata Digest CSV

Version 2: Updated 2017/12/06

Downloads require the NBIA Data Retriever .

Added associated DICOM SEG, SR, and RWV objects

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
DICOM Metadata Digest CSV

Version 1: Updated 2015/08/20

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
DICOM Metadata Digest CSV