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SPIE-AAPM Lung CT Challenge | SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset

DOI: 10.7937/K9/TCIA.2015.UZLSU3FL | Page Accessibility: Public | Collection

Collection Snapshot
Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Lung Human 70 CT Lung Cancer 36GB Clinical, Image Analyses Public, Complete 2023/09/13

Summary

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.

  • Release date of calibration set cases with truth:  November 21, 2014
  • Release date of test set cases without truth:  January 9, 2015
  • Submission date for participants to submit test set classification results:  February 6, 2015
  • SPIE Medical Imaging meeting:  February 21 to 26, 2015

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

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Title Data Type Format Access Points Studies Series Images License
Images CT DICOM 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.

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

Citations & Data Usage Policy

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

Publication Citation

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

Publication Citation

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

TCIA Citation

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. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

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