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

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Dataset Citation

Goldgof Dmitry, Hall Lawrenc , Hawkins Samuel, Schabath Matthew, Stringfield Olya, Garcia Alberto, Balagurunathan Yoganand, Kim Jongphil, Eschrich Steven, Berglund Anders, Gatenby Robert, Gillies Robert. (2017) Long and Short Survival in Adenocarcinoma Lung CTs. The Cancer Imaging Archive. http://doi.org

Description

The data set, which is the upper and lower quartile of eighty-one patients, consists of chest CT images from the H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida. It was imaged with standard-of-care contrast-enhanced CT scans of patients who had non-small cell cancer with biopsy-verified adenocarcinoma and used for survival time analysis with 2 years of follow-up. In stage one, there are 32 cases. In the second, third, and fourth stages, there are 20, 25, and 4, respectively.

For the eighty-one cases, slice thicknesses are from 2.5mm to 6mm, and the average thickness of a slice is 4.75mm. Excepting one scanner, all of the scanners were GE and Siemens. No observed relationship was found between survival time and slice thickness or the type of scanner. A region-growing algorithm segmented the tumor with seed points that were chosen by radiologists.

The adenocarcinoma cases are divided into the upper and lower quartiles of survival. Both the lower and upper quartiles have 20 cases. The lower quartile survival timeline is 103 to 498 days while the upper quartile timeline is 1351 to 2163 days. The average survival of the lower and upper quartiles is 288 days and 1569 days respectively. The median survival for the lower and upper quartiles is 289 and 1551 days respectively. The overall mean survival time is 879 days and median survival time is 925 days.

 

 The following two publications used this dataset:

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.

 

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.

 

Publication Citation

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  • Image Data -- DICOM
  • Clinical Data
  • Gene Expression Data

Segmentations zip file

Labels.csv including QIN-IDs

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