Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Lung micronodules: automated method for detection at thin-section CT--initial experience.

Matthew S Brown1, Jonathan G Goldin, Robert D Suh

  • 1Department of Radiology, David Geffen School of Medicine at UCLA, 10833 Le Conte Ave, Los Angeles, CA 90095-1721, USA. mbrown@mednet.ucla.edu

Radiology
|January 4, 2003
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Airway Mucus Plugs in Asthma and COPD: Pathobiology, Imaging, and Implications for Clinical Trials.

American journal of respiratory and critical care medicine·2026
Same author

The American Cancer Society National Lung Cancer Roundtable strategic plan: Challenges in clinical guideline recommendations for biomarker testing in lung cancer.

Cancer·2026
Same author

Evaluating the robustness of features generated by a foundation model from CT with different reconstruction parameters.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Assessing the impact of CT reconstruction kernel on radiomic features extracted from normal and fibrotic tissue in patients with diffuse lung disease.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

A Generative Model of Lung CT Conditioned on Radiomics Features.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Toward the simultaneous detection of multiple diseases with a highly cost-effective cell-free DNA methylome test.

Proceedings of the National Academy of Sciences of the United States of America·2026

An automated system significantly enhances lung micronodule detection on CT scans. It boosts radiologist sensitivity, particularly for smaller nodules, improving diagnostic accuracy.

Area of Science:

  • Medical Imaging
  • Radiology
  • Computer-Aided Diagnosis

Background:

  • Early detection of lung nodules is crucial for effective treatment.
  • Manual interpretation of computed tomographic (CT) images can be challenging for small lung micronodules.
  • Existing methods require significant radiologist effort and expertise.

Purpose of the Study:

  • To develop and evaluate an automated system for detecting lung nodules and micronodules on thin-section CT images.
  • To assess the system's performance independently and in assisting a radiologist.
  • To determine the impact of the automated system on diagnostic sensitivity.

Main Methods:

  • Development of an automated detection system for lung micronodules.
  • Application of the system to thin-section CT data from 15 subjects (77 lung nodules).

Related Experiment Videos

  • Comparison of automated system performance, radiologist performance, and radiologist performance with system assistance.

    • Main Results:

    • The automated system achieved 100% sensitivity for nodules >3 mm and 70% for micronodules <=3 mm without user interaction.
    • A radiologist achieved 91% sensitivity for nodules and 51% for micronodules.
    • With system assistance, radiologist sensitivity increased to 95% for nodules and 74% for micronodules.

    Conclusions:

    • The automated system demonstrates high sensitivity in detecting lung nodules, especially larger ones.
    • Computer-aided detection significantly improves radiologist performance in identifying lung micronodules.
    • This automated system shows promise for enhancing lung cancer screening and diagnosis.