SPIE-AAPM Lung CT Challenge | SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset
DOI: 10.7937/K9/TCIA.2015.UZLSU3FL | Data Citation Required | Image Collection
Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated | |
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Lung | Human | 70 | CT | Lung Cancer | Clinical, Image Analyses | Public, Complete | 2016/09/23 |
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
Version 2: Updated 2016/09/23
Added diagnosis data to test set XLS.
Title | Data Type | Format | Access Points | Subjects | License | |||
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Images | CT | DICOM | Download requires NBIA Data Retriever |
70 | 70 | 70 | 22,489 | CC BY 3.0 |
Nodule Locations/Diagnoses - Calibration Set | XLS | CC BY 3.0 | ||||||
Nodule Locations/Diagnoses - Test Set | XLS | CC BY 3.0 |
Additional Resources for this Dataset
The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.
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 |
|
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 |
Detailed Description
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
- CalibrationSet_NoduleData.xlsx – Nodule locations and diagnoses
- TestSet_NoduleData.xlsx – Nodule locations; diagnoses to be added after manuscript publication
Related Publications
Publications by the Dataset Authors
The authors recommended this paper as the best source of additional information about this dataset:
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
No publications by dataset authors were found.
Research Community Publications
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Previous Versions
Version 1: Updated 2014/11/21
Title | Data Type | Format | Access Points | License | ||||
---|---|---|---|---|---|---|---|---|
Images | DICOM | Download requires NBIA Data Retriever |
||||||
Nodule Locations/Diagnoses - Calibration Set | XLS | |||||||
Nodule Locations - Test Set | XLSX |