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

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 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.

 


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titlePublication Citation

 Hawkins, Samuel H., John N. Korecki, Yoganand Balagurunathan, Yuhua Gu, Virendra Kumar, Satrajit Basu, Lawrence O. Hall, Dmitry B. Goldgof, Robert A. Gatenby, and Robert J. Gillies. "Predicting Outcomes of Nonsmall Cell Lung Cancer using CT Image Features." IEEE Access 2 (2014): 1418-1426. DOI: 10.1109/ACCESS.2014.2373335

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Info
titlePublication Citation

Paul, Rahul, Samuel H. Hawkins, Yoganand Balagurunathan, Matthew B. Schabath, Robert J. Gillies, Lawrence O. Hall, and Dmitry B. Goldgof. "Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma." Tomography: a journal for imaging research 2, no. 4 (2016): 388. DOI:10.18383/j.tom.2016.00211

 


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Note: This data is restricted against commercial use.  Please contact help@cancerimagingarchive.net  with any questions on usage.

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