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LUNGCT-DIAGNOSIS

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

DOI: 10.7937/K9/TCIA.2015.A6V7JIWX | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Lung Human 61 CT Lung Cancer 2.47GB Clinical, Image Analyses Public, Complete 2014/12/30

Summary

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).

Data Access

Version 1: Updated 2014/12/30

Title Data Type Format Access Points Subjects Studies Series Images License
Images CT DICOM
Download requires NBIA Data Retriever
 61 61 61 4,682 CC BY 3.0
DICOM Metadata Digest CSV CC BY 3.0
Representative Tumor Slices XLS CC BY 3.0
Clinical Data DOC CC BY 3.0

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.

  • Imaging Data Commons (IDC) (Imaging Data)

    • 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

    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

    Detailed Description

    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).  

    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.

    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.

    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. (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

    Analysis Results Using this Collection

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

    Analysis Results Using This Collection