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

Computer-aided diagnosis for lung cancer.

A P Reeves1, W J Kostis

  • 1School of Electrical Engineering, Cornell University, Ithaca, New York, USA. reeves@ee.cornell.edu

Radiologic Clinics of North America
|June 16, 2000
PubMed
Summary

Computer-aided diagnosis (CAD) shows promise for lung cancer detection. While chest X-ray (CXR) CAD tools face limitations, CT CAD systems offer superior detail for improved accuracy in identifying pulmonary nodules.

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

AOtools: a Python package for adaptive optics modelling and analysis.

Optics express·2019
Same author

Inherent Nonrandom Structure of the Pre-Nuclear Envelope Breakdown Calcium Signal in Sand Dollar (Echinaracnius parma) Embryos.

The Biological bulletin·2017
Same author

Identification of Phospholipase A<sub>2</sub> and Phospholipase C Activities in Calcium Regulatory Endomembranes of Sand Dollar Cells.

The Biological bulletin·2017
Same author

Statistical Image Analysis of Spatial and Temporal Patterns in the Pre-Nuclear Envelope Breakdown Calcium Signal.

The Biological bulletin·2017
Same author

Local noise estimation in low-dose chest CT images.

International journal of computer assisted radiology and surgery·2013
Same author

Computational cost of image registration with a parallel binary array processor.

IEEE transactions on pattern analysis and machine intelligence·2011

Area of Science:

  • Radiology
  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • Computer-aided diagnosis (CAD) tools are being developed to enhance lung cancer diagnosis.
  • Current CAD systems for chest X-ray (CXR) are limited by the modality's inherent constraints.
  • Computed tomography (CT) offers greater detail, presenting an opportunity for more advanced CAD applications.

Purpose of the Study:

  • To explore the potential of CT-based CAD systems for lung cancer diagnosis.
  • To highlight the advantages of CT over CXR for CAD development.
  • To discuss the current state and future directions of CT CAD technology.

Main Methods:

  • Review of existing research and prototype CT CAD systems.
  • Discussion of technological advancements in CT scanners and their impact on CAD.
  • Consideration of knowledge-based engineering for robust CAD system development.

Main Results:

  • Initial CT CAD prototypes demonstrate high effectiveness in detecting small pulmonary nodules and predicting malignancy.
  • CT CAD systems can leverage the detailed information provided by CT scans.
  • Ongoing improvements in CT scanner technology are expected to enhance CAD accuracy and utility.

Conclusions:

  • CT CAD systems hold significant potential to improve radiologists' diagnostic accuracy for lung cancer.
  • Further research and development, particularly in knowledge-based engineering, are crucial for realizing the full capabilities of CT CAD.
  • Future CT CAD systems are anticipated to surpass current diagnostic practices, offering comprehensive analysis of lung conditions and nodules.

Related Experiment Videos