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HEAD-NECK-CETUXIMAB

The Cancer Imaging Archive

Head-Neck Cetuximab | Head-Neck Cetuximab

DOI: 10.7937/K9/TCIA.2015.7AKGJUPZ | Data Citation Required | 360 Views | 31 Citations | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Head-Neck Human 111 CT, PT, RTSTRUCT, RTPLAN, RTDOSE, Other, Protocol Head and Neck Carcinomas 52.36GB Image Analyses Limited, Complete 2013/11/14

Summary

This collection combines advanced molecular imaging treatment response assessment through pre- and post-treatment FDG PET/CT scans with therapy of advanced head and neck cancer, including chemo-radiation therapy with and without addition of an EGFR inhibitor molecular targeted agent (Cetuximab). 

The Head-Neck Cetuximab collection consists of a subset of image data from RTOG 0522, which was randomized phase III Trial of Radiation Therapy and Chemotherapy for stage III and IV Head and Neck carcinomas. The RTOG 0522 protocols were activated in November 2005 and successfully completed accrual of 945 patients in 2009. As part of the RTOG 0522 trial, CT, Structures, RT Doses, RT Plans were collected by RTOG, and institutions had the option to join a related quantitative PET (PET/CT) imaging study with ACRIN. The post-treatment FDG PET/CT scan was performed 8-9 weeks after completion of treatment before any nodal dissection. 

For more information about the original aims of this trial please see https://clinicaltrials.gov/ct2/show/results/NCT00265941?term=rtog0522 and this PDF.

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 1: Updated 2013/11/14

Title Data Type Format Access Points Subjects Studies Series Images License
Images and Radiation Therapy Structures CT, PT, RTSTRUCT, RTPLAN, RTDOSE DICOM
Download requires NBIA Data Retriever
 111 368 1,682 202,574 TCIA Restricted
Head-Neck Cetuximab (RTOG 0522) Trial Protocol Protocol PDF CC BY 3.0
DICOM Metadata Digest Other CSV CC BY 3.0
Analysis Results Using This Collection
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Additional Resources for this Dataset

The following external resources are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.

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

Bosch, W. R., Straube, W. L., Matthews, J. W., & Purdy, J. A. (2015). Head-Neck Cetuximab [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.7AKGJUPZ

Detailed Description

Supporting Documentation and metadata

Please note that 10 cases in this collection do not contain RT data.

Eight cases whose RT QA scores that were not “Per Protocol” or “Variation Acceptable” were excluded:  96, 133, 141, 143, 154, 182, 475, 478.

Also, subject 243 was ineligible and subject 260 expired prior to follow-up.

Related Publications

Publications by the Dataset Authors

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

Publication Citation

Ang, K. K., Zhang, Q., Rosenthal, D. I., Nguyen-Tan, P. F., Sherman, E. J., Weber, R. S., Galvin, J. M., Bonner, J. A., Harris, J., El-Naggar, A. K., Gillison, M. L., Jordan, R. C., Konski, A. A., Thorstad, W. L., Trotti, A., Beitler, J. J., Garden, A. S., Spanos, W. J., Yom, S. S., & Axelrod, R. S. (2014). Randomized Phase III Trial of Concurrent Accelerated Radiation Plus Cisplatin With or Without Cetuximab for Stage III to IV Head and Neck Carcinoma: RTOG 0522. In Journal of Clinical Oncology (Vol. 32, Issue 27, pp. 2940–2950). American Society of Clinical Oncology (ASCO). https://doi.org/10.1200/jco.2013.53.5633

No other publications were recommended by dataset authors.

Publication Citation

Ang, K. K., Zhang, Q., Rosenthal, D. I., Nguyen-Tan, P. F., Sherman, E. J., Weber, R. S., Galvin, J. M., Bonner, J. A., Harris, J., El-Naggar, A. K., Gillison, M. L., Jordan, R. C., Konski, A. A., Thorstad, W. L., Trotti, A., Beitler, J. J., Garden, A. S., Spanos, W. J., Yom, S. S., & Axelrod, R. S. (2014). Randomized Phase III Trial of Concurrent Accelerated Radiation Plus Cisplatin With or Without Cetuximab for Stage III to IV Head and Neck Carcinoma: RTOG 0522. In Journal of Clinical Oncology (Vol. 32, Issue 27, pp. 2940–2950). American Society of Clinical Oncology (ASCO). https://doi.org/10.1200/jco.2013.53.5633

Research Community Publications

TCIA maintains a list of publications which leverage our data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

  1. AlZu’bi, Shadi et al. “Transferable Hmm Probability Matrices in Multi‐Orientation Geometric Medical Volumes Segmentation.” Concurrency and Computation: Practice and Experience, 2019, p. e5214, doi:10.1002/cpe.5214.
  2. Edwards, Samuel et al. “Automated 3-D Tissue Segmentation Via Clustering.” Journal of Biomedical Engineering and Medical Imaging, vol. 5, no. 2, 2018, p. 08, doi: 10.14738/jbemi.52.4204.
  3. Gruselius, H. (2018).  Generative models and feature extraction on patient images and structure data in radiation therapy. Retrieved from  http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1215620&dswid=2429
  4. Ryalat MH, Laycock S, Fisher M, editors.  Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. International Conference on Bioinformatics and Biomedical Engineering; 2017: Springer. DOI:  10.1007/978-3-319-56148-6_37
  5. Sando, Yusuke et al. “Real-Time Interactive Holographic 3d Display with a 360 Degrees Horizontal Viewing Zone.” Appl Opt, vol. 58, no. 34, 2019, pp. G1-G5, doi:10.1364/AO.58.0000G1.
  6. Scarpelli, M. et al. “Optimal Transformations Leading to Normal Distributions of Positron Emission Tomography Standardized Uptake Values.” Phys Med Biol, vol. 63, no. 3, 2018, p. 035021, doi:10.1088/1361-6560/aaa175
  7. Sinha, A. et al. “The Deformable Most-Likely-Point Paradigm.” Med Image Anal, vol. 55, 2019, pp. 148-164, doi:10.1016/j.media.2019.04.013.
  8. Sinha, A. et al. “Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates.” First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, edited by Hayit Greenspan et al., Springer, 2019. doi:10.1007/978-3-030-32689-0_12.
  9. Tang, Hao et al. “Clinically Applicable Deep Learning Framework for Organs at Risk Delineation in Ct Images.” Nature Machine Intelligence, vol. 1, no. 10, 2019, pp. 480-491, doi:10.1038/s42256-019-0099-z.
  10. Teske, Hendrik et al. “Handling Images of Patient Postures in Arms up and Arms Down Position Using a Biomechanical Skeleton Model.” Current Directions in Biomedical Engineering, vol. 3, no. 2, 2017, pp. 469-472, doi:10.1515/cdbme-2017-0099.
  11. Wong, Jordan et al. “Comparing Deep Learning-Based Auto-Segmentation of Organs at Risk and Clinical Target Volumes to Expert Inter-Observer Variability in Radiotherapy Planning.” Radiother Oncol, vol. 144, 2019, pp. 152-158, doi:10.1016/j.radonc.2019.10.019.
  12. Zhu, Wentao. “Deep Learning for Automated Medical Image Analysis.” Computer Science, vol. Ph.D, University of California, Irvine, 15 March 2019 2019. general editor, Xiaohui Xie et al., https://arxiv.org/pdf/1903.04711.pdf.
  13. Zhu, Wentao et al. “Anatomynet: Deep Learning for Fast and Fully Automated Whole‐Volume Segmentation of Head and Neck Anatomy.” Medical Physics, vol. 46, no. 2, 2018, pp. 576-589, doi:https://doi.org/10.1002/mp.13300

 

The authors recommend the below publications describing the data:

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 our data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

  1. AlZu’bi, Shadi et al. “Transferable Hmm Probability Matrices in Multi‐Orientation Geometric Medical Volumes Segmentation.” Concurrency and Computation: Practice and Experience, 2019, p. e5214, doi:10.1002/cpe.5214.
  2. Edwards, Samuel et al. “Automated 3-D Tissue Segmentation Via Clustering.” Journal of Biomedical Engineering and Medical Imaging, vol. 5, no. 2, 2018, p. 08, doi: 10.14738/jbemi.52.4204.
  3. Gruselius, H. (2018).  Generative models and feature extraction on patient images and structure data in radiation therapy. Retrieved from  http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1215620&dswid=2429
  4. Ryalat MH, Laycock S, Fisher M, editors.  Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. International Conference on Bioinformatics and Biomedical Engineering; 2017: Springer. DOI:  10.1007/978-3-319-56148-6_37
  5. Sando, Yusuke et al. “Real-Time Interactive Holographic 3d Display with a 360 Degrees Horizontal Viewing Zone.” Appl Opt, vol. 58, no. 34, 2019, pp. G1-G5, doi:10.1364/AO.58.0000G1.
  6. Scarpelli, M. et al. “Optimal Transformations Leading to Normal Distributions of Positron Emission Tomography Standardized Uptake Values.” Phys Med Biol, vol. 63, no. 3, 2018, p. 035021, doi:10.1088/1361-6560/aaa175
  7. Sinha, A. et al. “The Deformable Most-Likely-Point Paradigm.” Med Image Anal, vol. 55, 2019, pp. 148-164, doi:10.1016/j.media.2019.04.013.
  8. Sinha, A. et al. “Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates.” First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, edited by Hayit Greenspan et al., Springer, 2019. doi:10.1007/978-3-030-32689-0_12.
  9. Tang, Hao et al. “Clinically Applicable Deep Learning Framework for Organs at Risk Delineation in Ct Images.” Nature Machine Intelligence, vol. 1, no. 10, 2019, pp. 480-491, doi:10.1038/s42256-019-0099-z.
  10. Teske, Hendrik et al. “Handling Images of Patient Postures in Arms up and Arms Down Position Using a Biomechanical Skeleton Model.” Current Directions in Biomedical Engineering, vol. 3, no. 2, 2017, pp. 469-472, doi:10.1515/cdbme-2017-0099.
  11. Wong, Jordan et al. “Comparing Deep Learning-Based Auto-Segmentation of Organs at Risk and Clinical Target Volumes to Expert Inter-Observer Variability in Radiotherapy Planning.” Radiother Oncol, vol. 144, 2019, pp. 152-158, doi:10.1016/j.radonc.2019.10.019.
  12. Zhu, Wentao. “Deep Learning for Automated Medical Image Analysis.” Computer Science, vol. Ph.D, University of California, Irvine, 15 March 2019 2019. general editor, Xiaohui Xie et al., https://arxiv.org/pdf/1903.04711.pdf.
  13. Zhu, Wentao et al. “Anatomynet: Deep Learning for Fast and Fully Automated Whole‐Volume Segmentation of Head and Neck Anatomy.” Medical Physics, vol. 46, no. 2, 2018, pp. 576-589, doi:https://doi.org/10.1002/mp.13300

 

The authors recommend the below publications describing the data: