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).
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.
Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.
|Data Type||Download all or Query/Filter|
|Images (DICOM, 2.5GB)|
|DICOM Metadata Digest (CSV)|
|Representative Tumor Slices (XLS)|
|Clinical Data (DOC)|
Click the Versions tab for more info about data releases.
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:
Number of Patients
Number of Studies
Number of Series
Number of Images
|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
This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to email@example.com.
Please be sure to include the following citations in your work if you use this data set:
Grove, Olya, Berglund, Anders E., Schabath, Matthew B., Aerts, Hugo J.W.L., Dekker, Andre, Wang, Hua, … Gillies, Robert J. (2015). Data from: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.A6V7JIWX
Grove, O., Berglund, A. E., Schabath, M. B., Aerts, H. J. W. L., Dekker, A., Wang, H., … Gillies, R. J. (2015, March 4). 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). http://doi.org/10.1371/journal.pone.0118261
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. (paper)
Other Publications Using This Data
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