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  • Long and Short Survival in Adenocarcinoma Lung CTs (LUAD-CT-Survival)

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Comment: updated citation formats, added shortname, all underlying data is untouched.

Summary

The dataset consists of pre-surgical chest CT images of 40 subjects from the H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida. The CT images were acquired by standard-of-care, contrast-enhanced CT scans among patients who had non-small cell cancer with biopsy-verified adenocarcinoma with 2 years of follow-up. A region-growing algorithm segmented the tumor with seed points that were chosen by radiologists.

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Localtab Group



Localtab
activetrue
titleData Access

Data Access

Click the Download  button to save the data.

Data TypeDownload all or Query/Filter
Images (DICOM)

Image Added

Click the Download  button to save a ".tcia" manifest file to your computer, which you must open with the  NBIA Data Retriever

Data TypeDownload all or Query/Filter
Images (DICOM)

Image Removed

Tumor Segmentations (NIFTINIfTI)

Image Features and Patient Survival (CSV)


Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:





Localtab
titleDetailed Description

Detailed Description

Image data is available in DICOM format. Segmentation data is available in .nii format. Labels are available in .csv format. The first column is subject identification. The second column is survival class.  Subsequent columns are computed image features which are described in the following publications.




Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

Public collection license

Info
titleDataset Citation

Goldgof D., Hall L., Hawkins S.H., Schabath M.B., Stringfield O., Garcia A., Balagurunathan Y., Kim J., Eschrich S., Berglund A.E., Gatenby R., Gillies R.J. (2017) Long and Short Survival in Adenocarcinoma Lung CTs. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.0tv7b9x1




Info
titleTCIA 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. https://doi.org/10.1007/s10278-013-9622-7


In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:


Info
titlePublication Citation

Paul, R., Hawkins, S.H. , Balagurunathan, Y., Schabath, M.B., Gillies, R.J. , Hall, L.O., & Goldgof, D. B(2016). . "Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among among Patients with Lung Adenocarcinoma. "  Tomography: a journal for imaging research 2, no. 4 (2016): 388. DOI:, 2(4), 388–395. https://doi.org/10.18383/j.tom.2016.00211


Info
titlePublication Citation

 Hawkins, S. H., Korecki, J. N., Balagurunathan, Y., Yuhua Gu Y., Kumar, V., Basu, S., Hall, L. O., Goldgof, D. B., Gatenby, R. A.,   & Gillies, R. J. (2014). " Predicting Outcomes of Nonsmall Cell Lung Cancer using Using CT Image Features. "  IEEE Access 2 (2014): 1418-1426. DOI: , 2, 1418–1426. https://doi.org/10.1109/ACCESSaccess.2014.2373335


Other Publications Using This Data

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




Localtab
titleVersions

Version 1 (Current): 2017/08/11


Data TypeDownload all or Query/Filter
Images (DICOM)

Tumor Segmentations (NIFTI)

Image Features and Patient Survival (CSV)





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