Dataset Citation
Goldgof Dmitry, Hall Lawrence, 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/10.7937/K9/TCIA.2017.0tv7b9x1
Description
The dataset consists of pre-surgical chest CT images 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. Among the 81 patients, 32 had stage I disease, 20 had stage II, 25 had stage III, and 4 had stage IV.
The CT slice thickness ranged from 2.5mm to 6mm; the average thickness of a slice is 4.75mm. All of the scanners were either GE or Siemens (expect for 1). We found no association between slice thickness and survival or between type of scanner and survival time. 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.
Publication 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
Publication 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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- Image Data -- DICOM
- Segmentations zip file
- Labels.csv including QIN-IDs