Summary

As part of the 2015 SPIE Medical Imaging Conference, SPIE – with the support of American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI) – will conduct a “Grand Challenge” on quantitative image analysis methods for the diagnostic classification of malignant and benign lung nodules. The LUNGx Challenge will provide a unique opportunity for participants to compare their algorithms to those of others from academia, industry, and government in a structured, direct way using the same data sets.

For more information please refer to: LUNGx SPIE-AAPM-NCI Lung Nodule Classification Challenge, the related SPIE Guest Editorial, and corresponding scientific manuscript.


Data Access

Data TypeDownload all or Query/FilterLicense 
Images (DICOM, 12.1GB)





 

(Download requires the NBIA Data Retriever)

Nodule Locations/Diagnoses - Calibration Set (XLS, )




Nodule Locations/Diagnoses - Test Set (XLS, 55 kB)




Click the Versions tab for more info about data releases.


Detailed Description

Collection Statistics


Modalities

CT

Number of Patients

70

Number of Studies

70

Number of Series

70

Number of Images

22,489

Images Size (GB)12.1

For more information please refer to: LUNGx SPIE-AAPM-NCI Lung Nodule Classification Challenge, the related SPIE Guest Editorial, and the follow up scientific manuscript.

Counts below reflect both the training set (10 subjects) and test set (60 subjects). The Patient IDs of the 10-subject training set begin CT-Training. The Patient IDs of the 60-subject test set begin LUNGx.

Nodule locations and diagnoses


Citations & Data Usage Policy 

Armato III, Samuel G.; Hadjiiski, Lubomir; Tourassi, Georgia D.; Drukker, Karen; Giger, Maryellen L.; Li, Feng; Redmond, George; Farahani, Keyvan; Kirby, Justin S.; Clarke, Laurence P. (2015). SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.UZLSU3FL


Armato III SG, Hadjiiski LM, Tourassi GD, Drukker K, Giger ML, Li F, Redmond G, Farahani K, Kirby JS, Clarke LP.  (2015). Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. Journal of Medical Imaging. SPIE-Intl Soc Optical Eng. DOI:  10.1117/1.jmi.2.2.020103


Samuel G. Armato, Karen Drukker, Feng Li, Lubomir Hadjiiski, Georgia D. Tourassi, Roger M. Engelmann, Maryellen L. Giger, George Redmond, Keyvan Farahani, Justin S. Kirby, Laurence P. Clarke. (2016)  "LUNGx Challenge for computerized lung nodule classification," J. Med. Imag. 3(4), 044506. DOI:  10.1117/1.JMI.3.4.044506


Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. (2013) The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6,  pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications which leverage our data. If you have a publication you'd like to add please contact TCIA's Helpdesk.


Version 2 (Current): Updated 2016/09/23

Data TypeDownload all or Query/Filter
Images (DICOM, 12.1GB)






(Download requires the NBIA Data Retriever)

Nodule Locations/Diagnoses - Calibration Set (XLS)




Nodule Locations/Diagnoses - Test Set (XLS)




Added diagnosis data to test set XLS.

Version 1: Updated 2014/11/21

Data TypeDownload all or Query/Filter
Images (DICOM, 12.1GB)




(Download requires the NBIA Data Retriever)

Nodule Locations/Diagnoses - Calibration Set (XLS)




Nodule Locations - Test Set (XLS)