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

Basic principles of ROC analysis.

C E Metz

    Seminars in Nuclear Medicine
    |October 1, 1978
    PubMed
    Summary
    This summary is machine-generated.

    Receiver Operating Characteristic (ROC) curve analysis offers a comprehensive method for evaluating diagnostic test performance beyond simple accuracy. It accounts for decision thresholds and aids in optimizing diagnostic strategies for better clinical decision-making.

    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

    Ideal observer approximation using Bayesian classification neural networks.

    IEEE transactions on medical imaging·2001
    Same author

    Components-of-variance models for random-effects ROC analysis: the case of unequal variance structures across modalities.

    Academic radiology·2001
    Same author

    Continuous versus categorical data for ROC analysis: some quantitative considerations.

    Academic radiology·2001
    Same author

    Computerized classification of benign and malignant masses on digitized mammograms: a study of robustness.

    Academic radiology·2000
    Same author

    Cost-effectiveness in radiology.

    European radiology·2000
    Same author

    Computer-aided diagnosis of pulmonary nodules: results of a large-scale observer test.

    Radiology·1999
    Same journal

    Letter from the Editors.

    Seminars in nuclear medicine·2026
    Same journal

    Expanding Horizons: The Role of Kaleidoscope and Relevant Images in Seminars in Nuclear Medicine.

    Seminars in nuclear medicine·2026
    Same journal

    The diagnostic performance and clinical value of [18F]FDG PET/CT in pleural mesothelioma - A systematic review and meta-analysis.

    Seminars in nuclear medicine·2026
    Same journal

    Feasibility of treating neuroendocrine prostate cancer with anti-SSTR radioligands: A systematic review of imaging and treatment studies.

    Seminars in nuclear medicine·2026
    Same journal

    <sup>18</sup>F-FDG -PET/CT in cardiac sarcoidosis: Diagnosis, therapy monitoring, and future directions.

    Seminars in nuclear medicine·2026
    Same journal

    Maximizing diagnostic yield: A systematic review and deep dive into PSMA PET scan protocol variations for prostate cancer.

    Seminars in nuclear medicine·2026
    See all related articles

    Area of Science:

    • Medical Diagnostics
    • Decision Analysis
    • Biostatistics

    Background:

    • Diagnostic accuracy limitations necessitate improved performance measures.
    • Sensitivity and specificity are more informative but threshold-dependent.
    • Receiver Operating Characteristic (ROC) curves address threshold effects.

    Purpose of the Study:

    • Introduce and explain Receiver Operating Characteristic (ROC) curve analysis.
    • Demonstrate ROC curves as a complete description of diagnostic decision thresholds.
    • Relate ROC analysis to cost-benefit analysis for optimizing diagnostic strategies.

    Main Methods:

    • Describing practical techniques for measuring ROC curves.
    • Discussing case selection and curve-fitting issues.

    Related Experiment Videos

  • Generalizing ROC analysis for complex diagnostic tasks.

    • Main Results:

    • ROC curves provide a complete empirical description of decision threshold effects.
    • ROC analysis naturally integrates with cost/benefit analysis.
    • Optimal diagnostic compromises can be identified using average diagnostic cost and net benefit.

    Conclusions:

    • ROC curve analysis is a superior method for evaluating diagnostic test performance.
    • ROC analysis facilitates the optimization of diagnostic strategies.
    • This framework supports informed clinical decision-making by balancing diagnostic errors.