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  • Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma (LungCT-Diagnosis)

Summary

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All the images are diagnostic contrast enhanced CT scans. The images were retrospectively acquired, to ensure sufficient patient follow-up. Slice thickness is variable : between 3 and 6 mm. All images were done at diagnosis and prior to surgery. The objective of the study was to extract prognostic image features that will describe lung adenocarcinomas and will associate with overall survival.  

Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity and intratumor density variation using routinely obtained diagnostic CT scans. The features systematically scored tumors and identified imaging phenotypes which exhibited survival differences. The features were extracted from routinely obtained CT images and were reproducible and stable despite the inherent clinical image acquisition variability. Our results suggest that quantitative imaging features can be used as an additional diagnostic tool in management of lung adenocarcinomas. More information is available in the related publication (see Citation tab below).

Acknowledgements

We would like to acknowledge the individual and institution that have provided data for this collection:

  • Moffitt Cancer Center (Tampa Florida) - Special thanks to Olya Stringfield, PhD  from the Department of Cancer Imaging and Metabolism.

Data Access

Data TypeDownload all or Query/FilterLicense
Images (DICOM, 2.5GB)

   

(Download requires the NBIA Data Retriever)

CC BY 3.0

DICOM Metadata Digest (CSV, 11 kB)

 

CC BY 3.0

Representative Tumor Slices (XLS, 16 kB)

 

CC BY 3.0

Clinical Data (DOC, 31 kB)

 

CC BY 3.0

Click the Versions tab for more info about data releases.

Additional Resources for this Dataset

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.

Third Party Analyses of this Dataset

TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:

Detailed Description

Collection Statistics

Updated 12/30/2014

Modalities

CT

Number of Patients

61

Number of Studies

61

Number of Series

61

Number of Images

4,682

Images Size (GB)2.5

TCIA DICOM Subject ID, SOP Instance UID, Instance Number, and Image Position (Patient) X-Y-Z  are noted in Representative-Tumor-Slices.xlsx

The accompanying data  are survival data (status: dead or alive, survival time in months) and pathological stage (TNM).  

Citations & Data Usage Policy 

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

Data Citation

Grove O, Berglund AE, Schabath MB, Aerts HJWL, Dekker A, Wang H, Velazquez ER, Lambin P, Gu Y, Balagurunathan Y, Eikman E, Gatenby RA, Eschrich S, Gillies RJ. (2015). Data from: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.A6V7JIWX

Publication Citation

Grove O, Berglund AE, Schabath MB, Aerts HJWL, Dekker A, Wang H, Velazquez ER, Lambin P, Gu Y, Balagurunathan Y, Eikman E, Gatenby RA, Eschrich S, Gillies RJ. (2015). Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma. (A. Muñoz-Barrutia, Ed.)PLOS ONE. Public Library of Science (PLoS). https://doi.org/10.1371/journal.pone.0118261

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. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057.DOI: https://doi.org/10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications that leverage our data.  If you have a publication you'd like to add, please contact the TCIA Helpdesk.

Version 1 (Current): Updated 2014/12/30

Data TypeDownload all or Query/Filter
Images (DICOM, 2.5GB)

   

(Download requires the NBIA Data Retriever)

DICOM Metadata Digest (CSV)

 

Representative Tumor Slices (XLS)

 

Clinical Data (DOC)

 


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