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The RADCURE dataset was collected clinically for radiation therapy treatment planning and retrospectively reconstructed for quantitative imaging research.  

Acquisition and Validation Methods: RADCURE is comprised of data for 3,346 patients and contains computed tomography (CT) images with corresponding normal and non-normal tissue contours. CT scans were collected using systems from three different manufacturers. Standard clinical imaging protocols were followed, and contours were generated and reviewed at weekly quality assurance rounds. RADCURE imaging and structure set data was extracted from our institution’s radiation treatment planning and oncology systems using an in-house data mining and processing system. Furthermore, images are linked to clinical data for each patient and includes demographic, clinical and treatment information based on the 7th edition TNM staging system. The median patient age is 63, with the final dataset including 80% males. Oropharyngeal cancer makes of 50% of the population with larynx, nasopharynx, and hypopharynx comprising 25, 12, and 5% respectively. Median follow-up was 5 years with 60% of the patients alive at last follow-up.   

Data Format and Usage Notes: During extraction of images and contours from our institution’s radiation treatment planning and oncology systems the data was converted to DICOM and RTSTRUCT formats, respectively. To improve the usability of the RTSTRUCT files, individual contour names were standardized for primary tumor volumes, and 29 organs-at-risk. Demographic, clinical, and treatment information is provided as a comma-separated values file. This dataset is a superset of the Radiomic Biomarkers in Oropharyngeal Carcinoma (OPC-Radiomics) dataset and fully encapsulates all previous data; this dataset replaces the OPC-Radiomics dataset. The RTSTRUCTs from OPC-Radiomics have been standardized to adhere to the TG-263 nomenclature. Age of 90 or greater is considered PHI and set to 90 to minimize impact to privacy. Both radiological and clinical metadata were offset by an undisclosed number of days for anonymization and should be noted for downstream analysis.

Potential Applications: The availability of imaging, clinical, demographic and treatment data in RADCURE makes it a viable option for a variety of quantitative image analysis research initiatives. This includes the application of machine learning or artificial intelligence methods to expedite routine clinical practices, discover new non-invasive biomarkers, or develop prognostic models.