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HNC-IMRT-70-33 | CT-RTSTRUCT-RTDOSE-RTPLAN Sets of Head and Neck Cancers Treated with Identical Prescriptions using IMRT: An Open Dataset for Deep Learning in Treatment Planning

DOI: 10.7937/ahqh-xc79 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Head-Neck Human 211 CT, RTDOSE, RTPLAN, RTSTRUCT Head and Neck Cancer 23.3GB Limited, Complete 2024/05/06


This collection includes data from 211 patients who presented with head and neck cancer and were treated using radiation therapy at a single institution using 6 MV Intensity Modulated Radiation Therapy (IMRT) from a linear accelerator. All patients were prescribed identical prescriptions of 70 Gray (Gy) in 33 fractions for the primary planning target volume (PTV) and one, none, or a combination of integrated boost sub targets (PTV 54 Gy, PTV 56 Gy, PTV 57 Gy, PTV 60 Gy, PTV 63 Gy, PTV 66 Gy).  The data for each patient contains the minimum required information for radiation treatment planning. Each patient set contains the planning computed tomography (CT) image set from treatment simulation, the expert-defined radiotherapy structure set (RTSTRUCT), the delivered radiotherapy plan file (RTPLAN), and the calculated treatment dose (RTDOSE). All files are provided in DICOM format. 

CT Images: Planning CT images from treatment simulation and radiation dose calculation are included for each patient. Thermoplastic head and neck masks were used during image acquisition to achieve immobilization and ensure accurate and reproducible positioning during treatment. All images satisfied the resolution requirements for dose calculations and have an identical slice thicknesses of 2.5 mm. The provided CT files are those that match with the RTDOSE and RTSTRUCT files of the same patient. 

Target and Organ Contours: Each patient’s data contains an RTSTRUCT DICOM with manually defined target volume contours and organ at risk (OAR) contours. For efficient OAR extraction from the RTSTRUCT files, we renamed all OAR contours that could be identified to a uniform structure name. If a contour was not identified, it was not defined for treatment, or the nomenclature was ambiguous. All planning target volumes (PTV) are included in the structure set. Due to inconsistent documentation and naming conventions, the primary PTV is difficult to distinguish from other PTV structures. For example, some patients include multiple iterations of the same contour as a result of modifications during treatment planning our assisted planning methods from the TPS. In addition to organ and target structures, we chose to leave in planning structures created and used by the dosimetrists for generating the plan. For example, PTV expansions, ring structures, avoidance structures, and normal tissue volumes were left within the RTSTRUCT file. OAR contours for 26 unique normal tissue structures were renamed to the ‘Standardized Name’ in the RTSTRUCT DICOM file. If a structure was not identified, the structure was not created in the original structure set. 

Radiotherapy Dose and Treatment Plan: Each dose was planned by a dosimetrist and approved for treatment by the physician. Treatment plans were created using Pinnacle (Phillips Medical Systems, Fitchburg, WI). The dose was calculated on a 3 mm3 dose grid and plans were optimized for treatment on Elekta or Varian linear accelerators.

Research for challenging tumor sites like head and neck cancers can benefit from publicly accessible radiotherapy treatment datasets such as the one offered here. In particular, this dataset provides consistent treatment plans with limited variance due to the uniformity in tumor site, consistent institutional standards, and identical prescriptions. In artificial intelligence research, the need for large and uniform datasets is crucial for testing and evaluating the performance of novel architectures. Treatments of a single site from a single institution and with identical prescriptions will have reduced variance of tumor coverage and normal tissue-sparing objectives in comparison to multi-site and multi-institutional studies. However, it can also be combined with other radiotherapy treatment datasets for use in more generalized studies.

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 before accessing the data.

Version 1: Updated 2024/05/06

Title Data Type Format Access Points Subjects Studies Series Images License
Download requires NBIA Data Retriever
211 211 844 38,032 TCIA Restricted
Standardized OAR Name List CSV 211 CC BY 4.0

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

Buatti, J., Kabat, C., Li, R., Sivabhaskar, S., de Oliveira, M., Papanikolaou, N., Stathakis, S., Paragios, N., & Kirby, N. (2024). CT-RTSTRUCT-RTDOSE-RTPLAN Sets of Head and Neck Cancers Treated with Identical Prescriptions using IMRT: An Open Dataset for Deep Learning in Treatment Planning (Version 1) [Data set]. The Cancer Imaging Archive.

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 the TCIA Helpdesk.

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.

Previous Versions

Version 1: Updated 2024/05/06

Title Data Type Format Access Points Studies Series Images License
Download requires NBIA Data Retriever
211 844 38,032 TCIA Restricted
HNC-IMRT-70-33 Standardized OAR Names CSV CC BY 4.0