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Related Experiment Videos

Receiver operator characteristic (ROC) analysis without truth.

R M Henkelman1, I Kay, M J Bronskill

  • 1Department of Medical Biophysics, University of Toronto, Ontario, Canada.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|January 1, 1990
PubMed
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A novel diagnostic imaging analysis method bypasses the need for difficult-to-obtain truth data. This new approach, using correlative information, offers results comparable to traditional Receiver Operator Characteristic (ROC) analysis.

Area of Science:

  • Medical Imaging Analysis
  • Diagnostic Test Evaluation
  • Statistical Modeling in Medicine

Background:

  • Receiver Operator Characteristic (ROC) analysis is standard for evaluating diagnostic imaging tests.
  • Traditional ROC analysis necessitates independent disease truth data, which is often challenging to acquire and may lack accuracy.

Purpose of the Study:

  • To introduce a new analytical method for diagnostic imaging tests that does not require independent truth data.
  • To enable comparison of multiple accurate diagnostic tests simultaneously.

Main Methods:

  • The new method employs correlative information to estimate multivariate normal distribution models for disease-positive and disease-negative patient groups.
  • It utilizes statistical modeling to infer disease states without external ground truth.

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Main Results:

  • The proposed method yielded results equivalent to conventional ROC analysis in a comparative study of computed tomography, radionuclide scintigraphy, and magnetic resonance imaging for liver metastasis.
  • Demonstrated comparable performance to established ROC analysis.

Conclusions:

  • A new statistical method allows for the evaluation of diagnostic imaging tests without relying on external truth data.
  • This approach can be extended to incorporate truth data or assess its consistency with imaging data when available.