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SPIE-AAPM-LUNG-CT-CHALLENGE

The Cancer Imaging Archive

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

DOI: 10.7937/K9/TCIA.2015.UZLSU3FL | Data Citation Required | 568 Views | 36 Citations | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Lung Human 70 CT, Diagnosis, Measurement Lung Cancer 12.07GB 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.

  • 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

Version 2: Updated 2016/09/23

Added diagnosis data to test set XLS.

Title Data Type Format Access Points Subjects Studies Series Images License
Images CT DICOM
Download requires NBIA Data Retriever
 70 70 70 22,489 CC BY 3.0
Nodule Locations/Diagnoses - Calibration Set Diagnosis, Measurement XLS CC BY 3.0
Nodule Locations/Diagnoses - Test Set Diagnosis, Measurement XLS CC BY 3.0
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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.

  • Imaging Data Commons (IDC) (Imaging 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

    Related Publications

    Publications by the Dataset Authors

    The authors recommended the following as the best source of additional information about this dataset:

    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

    No other publications were recommended by dataset authors.

    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

    Research Community Publications

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    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.

    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.

    Previous Versions

    Version 1: Updated 2014/11/21

    Title Data Type Format Access Points Subjects Studies Series Images License
    Images DICOM
    Download requires NBIA Data Retriever
    Nodule Locations/Diagnoses - Calibration Set XLS
    Nodule Locations - Test Set XLSX