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RADIOMICS-TUMOR-PHENOTYPES

The Cancer Imaging Archive

Radiomics-Tumor-Phenotypes | Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

DOI: 10.7937/K9/TCIA.2014..UA0JGPDG | Data Citation Required | Analysis Result

Cancer Types Location Subjects Related Collections Size Updated
Lung Cancer, Head and Neck Cancer Lung, Head-Neck 1,019 956.69MB 2020/03/23

Summary

This data applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer which are described in Nature Communications (http://doi.org/10.1038/ncomms5006).  The various arms of the study are represented in TCIA as distinct Collections including NSCLC-Radiomics (Lung1), NSCLC-Radiomics-Genomics (Lung3), Head-Neck-Radiomics-HN1 (H&N1), NSCLC-Radiomics-Interobserver1 (Multiple delineation), and RIDER-LungCT-Seg (RIDER test/retest).

Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present 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.

Data Access

Version 2: Updated

Added links to the recently published TCIA collections which reflect the additional arms of the study described in Nature Communications (http://doi.org/10.1038/ncomms5006).

Title Data Type Format Access Points Subjects Studies Series Images License
Gross Tumor Volume Segmentations from RIDER-LungCT-Seg SEG, RTSTRUCT DICOM
Download requires NBIA Data Retriever
31 43 118 118 CC BY 3.0

Collections Used In This Analysis Result

Title Data Type Format Access Points Subjects Studies Series Images License
Corresponding Original Images from Head-Neck-Radiomics-HN1 - H&N1 SEG, RTSTRUCT, CT, PT DICOM 137 137 486 28,918 TCIA No Commercial Limited
Corresponding Original Images from NSCLC-Radiomics, NSCLC-Radiomics-Genomics, NSCLC-Radiomics-Interobserver1 SEG, RTSTRUCT, CT DICOM 533 533 1,418 69,441 CC BY-NC 3.0
Corresponding Original Images from RIDER-LungCT-Seg - RIDER test/retest SEG, RTSTRUCT DICOM 31 43 118 118 CC BY 3.0

Collections Used In This Analysis Result

Related Collections

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

Aerts, H., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., & Lambin, P. (2014). Data from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (Radiomics-Tumor-Phenotypes). [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2014..UA0JGPDG

Publications Using This Data

TCIA maintains a list of publications that leverage our data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Publication Citation

Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., & Lambin, P. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 5(1). https://doi.org/10.1038/ncomms5006

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 2016/08/02

Title Data Type Format Access Points Subjects Studies Series Images License
Image Data DICOM
Clinical Data XLS and CSV
Gene Expression Data