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Leonard Wee, Hugo Aerts, Petros Kalendralis and Andre Dekker. (2018) RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2020.jit9grk8 |
Description
This collection dataset contains images from 32 non-small cell lung cancer (NSCLC) patients based on the public open image collection RIDER Lung CT on TCIA. For these subjects a radiation oncologist was blinded to the all delineations of the 3D volume of the gross tumor volume. They were then asked to manually delineate the gross tumour volume in both the test image and the re-test image. The process was repeated using an in-house autosegmentation method. There is no clinical outcome data associated with this dataset.
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(RIDER-2283289298) only has segmentations associated with the retest.
(RIDER-5195703382) only has segmentations associated with the test.
(RIDER-8509201188) only has segmentations associated with the test.
(RIDER-9762593735) not included in the data set due to missing delineations.
In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:
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Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Cavalho, S., … Lambin, P. (2014, June 3). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. Nature Publishing Group. http://doi.org/10.1038/ncomms5006 |
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Zhao, Binsheng, Schwartz, Lawrence H, & Kris, Mark G. (2015). Data From RIDER_Lung CT. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2015.U1X8A5NR |
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Zhao, B., James, L. P., Moskowitz, C. S., Guo, P., Ginsberg, M. S., Lefkowitz, R. A.,Qin, Y. Riely, G.J., Kris, M.G., Schwartz, L. H. (2009, July). Evaluating Variability in Tumor Measurements from Same-day Repeat CT Scans of Patients with Non–Small Cell Lung Cancer 1 . Radiology. Radiological Society of North America (RSNA). DOI: 10.1148/radiol.2522081593 (paper) |
Download
Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. This analysis set may not be used for commercial purposes. It is available to browse, download, as outlined in the Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) https://creativecommons.org/licenses/by-nc/3.0/. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.
Data Type | Download all or Query/Filter |
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Gross Tumor Volume Segmentation - 31 subjects (DICOM RTSTRUCT and SEG, 912 Mbytes) | |
Corresponding Original CT Images from RIDER Lung CT - 31 subjects (DICOM, 7 GBytes) |
