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

Variables affecting pulmonary nodule detection with computed tomography: evaluation with three-dimensional computer

D P Naidich1, H Rusinek, G McGuinness

  • 1Department of Radiology, New York University Medical Center, Bellevue Hospital, NY 10016.

Journal of Thoracic Imaging
|January 1, 1993
PubMed
Summary
This summary is machine-generated.

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

Global advances and future directions in lung cancer care: expert consensus and strategic priorities.

ESMO open·2026
Same author

Predicted Effect of Incidental Pulmonary Nodule Findings on NSCLC Mortality.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2024
Same author

Choroid Plexus Calcification Correlates with Cortical Microglial Activation in Humans: A Multimodal PET, CT, MRI Study.

AJNR. American journal of neuroradiology·2023
Same author

Preliminary Findings Associate Hippocampal <sup>1</sup>H-MR Spectroscopic Metabolite Concentrations with Psychotic and Manic Symptoms in Patients with Schizophrenia.

AJNR. American journal of neuroradiology·2020
Same author

<i>Reply</i>.

AJNR. American journal of neuroradiology·2017
Same author

Lumbar Puncture Test in Normal Pressure Hydrocephalus: Does the Volume of CSF Removed Affect the Response to Tap?

AJNR. American journal of neuroradiology·2017
Same journal

Editors' Recognition for Reviewing in 2025.

Journal of thoracic imaging·2026
Same journal

The Diagnostic and Prognostic Value of Multi-Chamber Strain Analysis by Cardiac MRI in Amyloidosis.

Journal of thoracic imaging·2026
Same journal

Computed Tomography-Derived Nakata Index as a Predictor of Functional Impairment in Chronic Obstructive Pulmonary Disease: A Retrospective Cohort Study.

Journal of thoracic imaging·2026
Same journal

Regarding the Society of Thoracic Radiology Mentoring Program.

Journal of thoracic imaging·2026
Same journal

Pulmonary Fungal Infections in Immunocompetent Patients.

Journal of thoracic imaging·2026
Same journal

The Air-Cleft Sign: Imaging and Histopathologic Findings in 7 Cases of Pulmonary Benign Metastasizing Leiomyomatosis.

Journal of thoracic imaging·2026
See all related articles

Computer-generated nodules help evaluate computed tomography (CT) accuracy for pulmonary nodule detection. Nodule size, location, and density significantly impact radiologist identification rates.

Area of Science:

  • Radiology
  • Medical Imaging
  • Pulmonary Medicine

Background:

  • Accurate identification of pulmonary nodules via computed tomography (CT) is crucial for diagnosing lung diseases.
  • Evaluating factors influencing CT accuracy for nodule detection is essential for optimizing diagnostic protocols.

Purpose of the Study:

  • To assess the impact of various nodule characteristics and technical CT parameters on the accuracy of pulmonary nodule identification.
  • To determine the utility of computer-generated nodules for evaluating imaging interpretation performance.

Main Methods:

  • Computer-generated pulmonary nodules were superimposed onto normal CT scans.
  • Three experienced chest radiologists independently interpreted the scans, evaluating nodule size, shape, density, location, and technical factors like slice thickness.

Related Experiment Videos

  • Statistical analysis, including linear discriminant function analysis, was used to identify significant factors.

    • Main Results:

    • Overall sensitivity for nodule identification was 62% and specificity was 80%.
    • Nodule detection rates varied significantly with nodule size (p < 0.001) and CT slice thickness (p = 0.037), with smaller nodules and thinner slices posing greater detection challenges.
    • Nodule location, angiocentricity, and density were significant factors influencing identification (p < 0.01).

    Conclusions:

    • Computer-generated nodules provide a valuable tool for assessing numerous imaging variables and improving the accuracy of pulmonary nodule detection.
    • This methodology can aid in optimizing three-dimensional (3D) scan protocols and evaluating interpretation accuracy for various thoracic pathologies.