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HEAD-NECK-PET-CT

Head-Neck-PET-CT | Head-Neck-PET-CT

DOI: 10.7937/K9/TCIA.2017.8oje5q00 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Head-Neck Human 298 CT, PT, REG, RTSTRUCT, RTPLAN, RTDOSE Head and Neck Cancer 72.46GB Clinical, Image Analyses, Software/Source Code Limited, Complete 2018/06/07

Summary

This collection contains FDG-PET/CT and radiotherapy planning CT imaging data of 298  patients from four different institutions in Québec with histologically proven head-and-neck cancer (H&N) All patients had pre-treatment FDG-PET/CT scans between April 2006 and November 2014, and within a median of 18 days (range: 6-66) before treatment. Dates in the TCIA images have been changed in the interest of de-identification; the same change was applied across all images, preserving the time intervals between serial scans.  These patients were all part of a study described in further detail  (treatment, image scanning protocols, etc.)  in the publication:

Vallières, M. et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep 7, 10117 (2017). doi: 10.1038/s41598-017-10371-5
 
 

Note:  Subsequent to publishing this manuscript it was discovered images from  two patients included in the analysis had errors and should not be used in future studies.  Therefore these have not been included in this TCIA data set, leaving 298 patients of the original 300 analyzed.

In the original study, 93 of the 300 patients (31 %), the radiotherapy contours were directly drawn on the CT of the FDG-PET/CT scan by expert radiation oncologists and thereafter used for treatment planning. For 207 of the 300 patients (69 %), the radiotherapy contours were drawn on a different CT scan dedicated to treatment planning and were propagated/resampled to the FDG-PET/CT scan reference frame using intensity-based free-form deformable registration with the software MIM® (MIM software Inc., Cleveland, OH).

Patients with recurrent H&N cancer or with metastases at presentation, and patients receiving palliative treatment were excluded from the study. From the 300 patients, 48 received radiation alone (16 %) and 252 received chemo-radiation (84 %) with curative intent as part of treatment management. The median follow-up period of all patients was 43 months (range: 6-112). Patients that did not develop a locoregional recurrence or distant metastases during the follow-up period and that had a follow-up time smaller than 24 months were also excluded from the study. During the follow-up period, 45 patients developed a locoregional recurrence (15 %), 40 patients developed distant metastases (13 %) and 56 patients died (19 %).  

We analyzed the FDG-PET and CT images of the 300 patients from four different cohorts for the risk assessment of locoregional recurrences (LR) and distant metastases in H&N cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups.

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 2: Updated 2018/06/07

Added 250 total DICOM series to 162 total subjects that had been missing.

Title Data Type Format Access Points Subjects Studies Series Images License
Images and Radiation Therapy Structures RTPLAN, RTDOSE, REG, RTSTRUCT, CT, PT DICOM
Download requires NBIA Data Retriever
 298 504 2,661 123,271 TCIA Restricted
Clinical Data XLSX 300 CC BY 3.0
Names of GTV contours XLSX 298 CC BY 3.0

Additional Resources for this Dataset

The following external resources have been made available by the data submitters.  These 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

Martin Vallières, Emily Kay-Rivest, Léo Jean Perrin, Xavier Liem, Christophe Furstoss, Nader Khaouam, Phuc Félix Nguyen-Tan, Chang-Shu Wang, Khalil Sultanem. (2017). Data from Head-Neck-PET-CT. The Cancer Imaging Archive. doi: https://doi.org/10.7937/K9/TCIA.2017.8oje5q00

Detailed Description

We hope the available data and source code will facilitate the standardization and reproducibility of methods in the radiomics community.

  • Clinical Data – This spreadsheet includes patient information, histopathological type, tumour grade, outcome follow-up information (metastases, survival), etc.
  • Names of GTV contours — This spreadsheet contains all the names of the “GTV primary” and “GTV lymph nodes” structures (as found in the associated RTstruct files) used in the publication of (Vallières et al., Sci Rep 7, 2017). Names of different structures are separated by commas in a given entry of the spreadsheet.

Note:  the images contain no private-vendor DICOM tags.

Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • McGill University, Montreal, Canada - Special thanks to Martin Vallières of the Medical Physics Unit

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.

  1. Babier, A., Zhang, B., Mahmood, R., Moore, K. L., Purdie, T. G., McNiven, A. L., & Chan, T. C. Y. (2021). OpenKBP: The open‐access knowledge‐based planning grand challenge and dataset. Medical Physics. doi: https://doi.org/10.1002/mp.14845.
  2. Cai, C., Lv, W., Chi, F., Zhang, B., Zhu, L., Yang, G., . . . Lu, L. (2022). Prognostic generalization of multi-level CT-dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma. Med Phys. doi: https://doi.org/10.1002/mp.16044.
  3. Chandrashekar, A., Handa, A., Ward, J., Grau, V., & Lee, R. (2022). A deep learning pipeline to simulate fluorodeoxyglucose (FDG) uptake in head and neck cancers using non-contrast CT images without the administration of radioactive tracer. Insights Imaging, 13(1), 45. doi:10.1186/s13244-022-01161-3.
  4. Chatterjee, A., Vallieres, M., Forghani, R., & Seuntjens, J. (2021). Investigating the impact of the CT Hounsfield unit range on radiomic feature stability using dual energy CT data. Phys Med, 88, 272-277. doi:10.1016/j.ejmp.2021.07.023.
  5. Clark, B., Hardcastle, N., Johnston, L. A., & Korte, J. (2024). Transfer learning for auto-segmentation of 17 organs-at-risk in the head and neck: Bridging the gap between institutional and public datasets. Med Phys. doi: https://doi.org/10.1002/mp.16997
  6. Diamant, A., Chatterjee, A., Vallières, M., Shenouda, G., & Seuntjens, J. (2019). Deep learning in head & neck cancer outcome prediction. Sci Rep, 9(1), 2764. doi:10.1038/s41598-019-39206-1.
  7. Ger, R. B., Zhou, S., Elgohari, B., Elhalawani, H., Mackin, D. M., Meier, J. G., . . . Court, L. E. (2019). Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients. PLoS One, 14(9), e0222509. doi: 10.1371/journal.pone.0222509
  8. Gouthamchand, V., Choudhury, A., Hoebers, F., Wesseling, F., Welch, M., Kim, S., . . . Wee, L. (2023). Privacy-Preserving Dashboard for F.A.I.R Head and Neck Cancer data supporting multi-centered collaborations. Paper presented at the 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2023), Basel, Switzerland. Retrieved from https://ceur-ws.org/Vol-3415//paper-2.pdf.
  9. Gouthamchand, V., Choudhury, A., Hoebers, F. J. P., Wesseling, F. W. R., Welch, M., Kim, S., . . . Wee, L. (2024). Making head and neck cancer clinical data Findable-Accessible-Interoperable-Reusable to support multi-institutional collaboration and federated learning. BJR|Artificial Intelligence, 1(1). doi: https://doi.org/10.1093/bjrai/ubae005.
  10. Haider, S. P., Mahajan, A., Zeevi, T., Baumeister, P., Reichel, C., Sharaf, K., . . . Payabvash, S. (2020). PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma. Eur J Nucl Med Mol Imaging. doi: https://doi.org/10.1007/s00259-020-04839-2.
  11. Haider, S. P., Sharaf, K., Zeevi, T., Baumeister, P., Reichel, C., Forghani, R., . . . Payabvash, S. (2020). Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics. Translational oncology, 14(1), 100906. doi: https://doi.org/10.1016/j.tranon.2020.100906.
  12. Haider, S. P., Zeevi, T., Baumeister, P., Reichel, C., Sharaf, K., Forghani, R., . . . Payabvash, S. (2020). Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma. Cancers (Basel), 12(7). doi: https://doi.org/10.3390/cancers12071778
  13. Han, K., Joung, J. F., Han, M., Sung, W., & Kang, Y. N. (2022). Locoregional Recurrence Prediction Using a Deep Neural Network of Radiological and Radiotherapy Images. J Pers Med, 12(2). doi:10.3390/jpm12020143
  14. Kim, J., Seo, S., Ashrafinia, S., Rahmim, A., Sossi, V., & Klyuzhin, I. (2019). Training of deep convolutional neural nets to extract radiomic signatures of tumors. Journal of Nuclear Medicine, 60. Retrieved from WOS:000473116800405.
  15. Kosareva, A. A., Paulenka, D. A., Snezhko, E. V., Bratchenko, I. A., & Kovalev, V. A. (2022). Examining the Validity of Input Lung CT Images Submitted to the AI-Based Computerized Diagnosis. Journal of Biomedical Photonics & Engineering, 8(3). doi: https://doi.org/10.18287/JBPE22.08.030307
  16. Le, Q. C., Arimura, H., Ninomiya, K., & Kabata, Y. (2020). Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients. Sci Rep, 10(1), 1-12. doi:10.1038/s41598-020-78338-7.
  17. Le, Q. C., Arimura, H., Ninomiya, K., Kodama, T., & Moriyama, T. (2022). Can Persistent Homology Features Capture More Intrinsic Information about Tumors from (18)F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients? Metabolites, 12(10). doi: https://doi.org/10.3390/metabo12100972
  18. Le, W. T., Vorontsov, E., Romero, F. P., Seddik, L., Elsharief, M. M., Nguyen-Tan, P. F., . . . Kadoury, S. (2022). Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks. Sci Rep, 12(1), 3183. doi: 10.1038/s41598-022-07034-5.
  19. Lombardo, E., Kurz, C., Marschner, S., Avanzo, M., Gagliardi, V., Fanetti, G., . . . Landry, G. (2021). Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts. Sci Rep, 11(1), 6418. doi: 10.1038/s41598-021-85671-y
  20. Lv, W., Xu, H., Han, X., Zhang, H., Ma, J., Rahmim, A., & Lu, L. (2022). Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer. Cancers (Basel), 14(7). doi: 10.3390/cancers14071674
  21. 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: https://doi.org/10.1016/j.cmpb.2023.107341
  22. Nikulin, P., Hofheinz, F., Maus, J., Li, Y., Butof, R., Lange, C., . . . van den Hoff, J. (2021). A convolutional neural network for fully automated blood SUV determination to facilitate SUR computation in oncological FDG-PET. Eur J Nucl Med Mol Imaging, 48(4), 995-1004. doi: 10.1007/s00259-020-04991-9
  23. Pan, Z., Men, K., Liang, B., Song, Z., Wu, R., & Dai, J. (2023). A subregion-based prediction model for local-regional recurrence risk in head and neck squamous cell carcinoma. Radiother Oncol, 109684. doi: 10.1016/j.radonc.2023.109684
  24. Prokopenko, D., Stadelmann, J. V., Schulz, H., Renisch, S., & Dylov, D. V. (2019). Unpaired Synthetic Image Generation in Radiology Using GANs. 11850, 94-101. doi:10.1007/978-3-030-32486-5_12
  25. Shi, F., Hu, W., Wu, J., Han, M., Wang, J., Zhang, W., . . . Shen, D. (2022). Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy. Nat Commun, 13(1), 6566. doi: 10.1038/s41467-022-34257-x
  26. Singh, A., Goyal, S., Rao, Y. J., & Loew, M. (2019). A Novel Imaging-Genomic Approach to Predict Outcomes of Radiation Therapy. (MS). George Washington University, Thesis.
  27. Singh, A., Goyal, S., Rao, Y. J., & Loew, M. (2019). Tumor Heterogeneity and Genomics to Predict Radiation Therapy Outcome for Head-and-Neck Cancer: A Machine Learning Approach. International Journal of Radiation Oncology*Biology*Physics, 105(1), S232-S233. doi: 10.1016/j.ijrobp.2019.06.334
  28. Tang, H., Chen, X., Liu, Y., Lu, Z., You, J., Yang, M., . . . Xie, X. (2019). Clinically applicable deep learning framework for organs at risk delineation in CT images. Nature Machine Intelligence, 1(10), 480-491. doi:10.1038/s42256-019-0099-z
  29. Teng, X. (2023). Improving radiomic model reliability and generalizability using perturbations in head and neck carcinoma. (Ph.D. Dissertation). Hong Kong Polytechnic University, Hong Kong Polytechnic University. Retrieved from https://theses.lib.polyu.edu.hk/handle/200/12547
  30. 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
  31. 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
  32. Varghese, A. J., Gouthamchand, V., Sasidharan, B. K., Wee, L., Sidhique, S. K., Rao, J. P., . . . Thomas, H. M. T. (2023). Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization. Phys Imaging Radiat Oncol, 26, 100450. doi: 10.1016/j.phro.2023.100450
  33. 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
  34. Wang, Y., Lombardo, E., Avanzo, M., Zschaek, S., Weingartner, J., Holzgreve, A., . . . Landry, G. (2022). Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis. Comput Methods Programs Biomed, 222, 106948. doi: 10.1016/j.cmpb.2022.106948
  35. Wu, A., Li, Y., Qi, M., Lu, X., Jia, Q., Guo, F., . . . Song, T. (2020). Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases. Oral Oncol, 104, 104625. doi: 10.1016/j.oraloncology.2020.104625
  36. Wu, H., Liu, X., Peng, L., Yang, Y., Zhou, Z., Du, D., . . . Lu, L. (2023). Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer. Phys Med Biol, 68(22). doi: 10.1088/1361-6560/ad03d1
  37. Yang, Q., Chao, H., Nguyen, D., & Jiang, S. (2019). A Novel Deep Learning Framework for Standardizing the Label of OARs in CT. Paper presented at the AIRT 2019: Artificial Intelligence in Radiation Therapy, Shenzhen, China. doi: 10.1007/978-3-030-32486-5_7
  38. Yang, R., Abdi, A. H., Eghbal, A., Wang, E., Tran, K. L., Yang, D., . . . Siewerdsen, J. H. (2021). Snake-based interactive tooth segmentation for 3D mandibular meshes. Paper presented at the Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, Online Only. doi: 10.1117/12.2581988
  39. Zhu, W. (2019). Deep Learning for Automated Medical Image Analysis. (Ph.D). University of California, Irvine, Retrieved from https://arxiv.org/pdf/1903.04711.pdf
  40. 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

Publication Citation

Vallières, M., Kay-Rivest, E., Perrin, L. J., Liem, X., Furstoss, C., Aerts, H. J. W. L., Khaouam, N., Nguyen-Tan, P. F., Wang, C.-S., Sultanem, K., Seuntjens, J., & El Naqa, I. (2017). Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. In Scientific Reports (Vol. 7, Issue 1).  DOI: https://doi.org/10.1038/s41598-017-10371-5

TCIA Citation

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

Previous Versions

Version 1: Updated 2017/11/30

Title Data Type Format Access Points Studies Series Images License
Images NB: 298 of DICOM
Clinical Data XLS
Names of GTV contours XLS
Source Code WEB