QIN-HEADNECK

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.

Citations & Data Usage Policy 

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

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

Version 4 (Current) : 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.

Version 3: Updated 2019/07/24

Lifted restriction from SR object data download.

Version 2: Updated 2017/12/06

Downloads require the NBIA Data Retriever .

Added associated DICOM SEG, SR, and RWV objects

Version 1: Updated 2015/08/20