LUAD-CT-Survival | Long and Short Survival in Adenocarcinoma Lung CTs
DOI: 10.7937/K9/TCIA.2017.0tv7b9x1 | Data Citation Required | 1 Citations | Analysis Result
| Location | Subjects | Size | Updated | |||
|---|---|---|---|---|---|---|
| Lung Adenocarcinoma | Chest | 40 | Tumor segmentations, radiomic image features | 2017/08/11 |
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. 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. Three of these cases, QIN-LSC-0009, QIN-LSC-0014, and QIN-LSC-0064 appear in LungCT-Diagnosis collection, while the remaining 37 cases are from the QIN Lung CT collection.
Data Access
Version 1: Updated 2017/08/11
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Tumor Segmentations | Segmentation | NIFTI and ZIP | 40 | 40 | CC BY 3.0 | |||
| Image Features and Patient Survival | Radiomic Feature, Follow-Up | CSV | CC BY 3.0 |
Collections Used In This Analysis Result
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Original Source Images | CT | DICOM | Requires NBIA Data Retriever |
40 | 40 | 40 | 3,142 | CC BY 3.0 |
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data Citation |
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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 [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.0tv7b9x1 |
Detailed Description
Image data is available in DICOM format. Segmentation data is available in .nii.gz 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:
- Paul, R., Hawkins, S., Balagurunathan, Y., Schabath, M., Gillies, R., Hall, L., & Goldgof, D. (2016). Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival among Patients with Lung Adenocarcinoma. Tomography, 2(4), 388–395. https://doi.org/10.18383/j.tom.2016.00211
- Hawkins, S. H., Korecki, J. N., Balagurunathan, Y., Yuhua Gu, 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 CT Image Features. IEEE Access, 2, 1418–1426. https://doi.org/10.1109/access.2014.2373335
Related Publications
Publications by the Dataset Authors
The authors recommended the following as the best source of additional information about this dataset:
Publication Citation |
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Paul, R., Hawkins, S., Balagurunathan, Y., Schabath, M., Gillies, R., Hall, L., & Goldgof, D. (2016). Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival among Patients with Lung Adenocarcinoma. Tomography, 2(4), 388–395. https://doi.org/10.18383/j.tom.2016.00211 |
Publication Citation |
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Hawkins, S. H., Korecki, J. N., Balagurunathan, Y., Yuhua Gu, 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 CT Image Features. IEEE Access, 2, 1418–1426. https://doi.org/10.1109/access.2014.2373335 |
Research Community Publications
TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.