Summary
The dataset consists of pre-surgical chest CT images of 40 subjects
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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 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 The CT images were acquired by standard-of-care, contrast-enhanced CT scans of among 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. 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.
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| title | Data Access |
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| Data AccessClick the Download button to save the data.
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| Tumor Segmentations (NIfTI, ZIP, 834 KB) |
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| url | https://wiki.cancerimagingarchive.net/download/attachments/24284406/segmentations.zip?api=v2 |
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| | | Image Features and Patient Survival (CSV, 107 KB) |
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| url | https://wiki.cancerimagingarchive.net/download/attachments/24284406/FeaturesWithLabels%20%281%29.csv?api=v2 |
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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: | Data Type | Download all or Query/Filter |
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| Original Source Images (DICOM, 1.54 GB) |
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| url | https://wiki.cancerimagingarchive.net/download/attachments/24284406/LongShortSurvivalAdenocarcinoma-doiJNLP-qOFdxYUi.tcia?api=v2 |
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(Download requires the NBIA Data Retriever) |
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| title | Detailed Description |
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| Detailed DescriptionImage 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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| title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy | Tcia limited license policy |
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| Publication Hawkins, Samuel Goldgof D., Hall L., Hawkins S.H., |
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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.2014.2373335Schabath 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 |
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| title | Publication Citation |
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| Paul, R., |
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Rahul Samuel H Hawkins Yoganand Matthew B Robert J Lawrence O and Dmitry B. ", D. (2016). Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival |
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Among among Patients with Lung Adenocarcinoma. |
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" : a journal for imaging research 2, no. 4 (2016): 388. DOI: |
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Note: This data is restricted against commercial use. Please contact help@cancerimagingarchive.net with any questions on usage.
- Image Data -- DICOM
- Clinical Data
- Gene Expression Data
Segmentations zip file
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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 |
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| 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 |
Other Publications Using This DataTCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
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| Version 1 (Current): 2017/08/11 | Data Type | Download all or Query/Filter |
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| Images (DICOM) | | | Tumor Segmentations (NIFTI) | | | Image Features and Patient Survival (CSV) | |
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