INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025
Evaluation, Agreement and Interpretation of Independent
Radiologist Assessments - ROC Analysis
Srikanth Chivukula; Dr. Uma Shankar; Dr. Raghunath Reddy
Rayalaseema University
Received: 10 November 2025; Accepted: 20 November 2025; Published: 06 December 2025
ABSTRACT:
ROC curves are useful for comparing two independent observations and the outcomes of their assessments for
the same disease in a given study. In general, the test with the higher AUC may be considered better and is an
effective way to summarize the overall diagnostic accuracy of the test. AUC scores are convenient to compare
multiple classifiers. Nonetheless, it is also important to check the actual curves especially when evaluating the
final model. In the ROC curve analysis, the choice of the optimal cutoff value depends both on probabilistic and
clinical considerations. From a probabilistic standpoint, one can use the coordinates of the ROC curve to identify
the cutoff that maximizes the discrimination between true-positive rate and false-positive rate(1).
The name "Receiver Operating Characteristic" came from. ROC analysis is part of a field called "Signal
Detection Theory" (3) developed during World War II for the analysis of radar images. It was not until the 1970's
that signal detection theory(4) was recognized as useful for interpreting medical test results.
Key Words: Sensitivity, Specificity, ROC curve, Area under the curve (AUC), Kappa.
INTRODUCTION:
ROC analysis originated in the early 1950's with electronic signal detection theory . The applications of ROC
methodology in diagnostic radiology dates back to the early 1960's. The first ROC curve in diagnostic radiology
was calculated by Lusted (1960) who re-analyzed the previously published data on the detection of pulmonary
tuberculosis and showed the reciprocal relationship between the percentage of false positive and of false negative
results from the different studies of chest film interpretations (2). Since then, several authors have used ROC
methodology to diagnostic imaging systems. The work of Dorfman and Alf (1968) was a pioneering step toward
objective curve fitting and the use of computerized software in ROC analysis (2). A maximum likelihood
approach under binormal assumption was developed in 1968 by Metz(6).
In the 1970s and 80s, the technique had considerable relevance to medical test evaluation and decision making,
and the decades followed seen much development and use of the technique in areas such as Life sciences,
radiology, cardiology, clinical chemistry, and epidemiology.
Now the applications have a much wider range and features in subjects as diverse as Clinical Research, Medical
devices, sociology, experimental psychology, atmospheric and earth sciences, finance, machine learning, and
data mining, among others.
The derived summary measure of accuracy of a diagnostic performance, one can compare individual tests or
judge whether the various combination of tests (e.g. combination of imaging techniques or combination of
readers) can improve diagnostic accuracy.
RESEARCH AND METHODS
In the context of the ROC curve analysis, the test with the higher AUC is considered better and is an effective
way to summarize the overall diagnostic accuracy of the test. The practical usefulness of ROC curves in
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