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
This dataset contains images from 31 out of the 32 non-small cell lung cancer (NSCLC) patients in the RIDER Lung CT collection 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.
This dataset refers to the RIDER dataset of the study published in Nature Communications (http://doi.org/10.1038/ncomms5006). In short, this publication used the dataset to select for repeatable radiomics features in a test-retest context. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In the published analysis, 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.
(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:
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
RIDER Lung CT Data Citation
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
RIDER Lung CT Publication Citation
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)
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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)