Disadvantages of Using the Area under the Receiver Operating Characteristic Curve to Assess Imaging Tests: A Discussion and Proposal for an Alternative Approach
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OBJECTIVES: The objectives are to describe the disadvantages of the area under the receiver operating characteristic curve (ROC AUC) to measure diagnostic test performance and to propose an alternative based on net benefit. METHODS: We use a narrative review supplemented by data from a study of computer-assisted detection for CT colonography. RESULTS: We identified problems with ROC AUC. Confidence scoring by readers was highly non-normal, and score distribution was bimodal. Consequently, ROC curves were highly extrapolated with AUC mostly dependent on areas without patient data. AUC depended on the method used for curve fitting. ROC AUC does not account for prevalence or different misclassification costs arising from false-negative and false-positive diagnoses. Change in ROC AUC has little direct clinical meaning for clinicians. An alternative analysis based on net benefit is proposed, based on the change in sensitivity and specificity at clinically relevant thresholds. Net benefit incorporates estimates of prevalence and misclassification costs, and it is clinically interpretable since it reflects changes in correct and incorrect diagnoses when a new diagnostic test is introduced. CONCLUSIONS: ROC AUC is most useful in the early stages of test assessment whereas methods based on net benefit are more useful to assess radiological tests where the clinical context is known. Net benefit is more useful for assessing clinical impact. KEY POINTS: • The area under the receiver operating characteristic curve (ROC AUC) measures diagnostic accuracy. • Confidence scores used to build ROC curves may be difficult to assign. • False-positive and false-negative diagnoses have different misclassification costs. • Excessive ROC curve extrapolation is undesirable. • Net benefit methods may provide more meaningful and clinically interpretable results than ROC AUC.
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Because ROC analysis doesn't consider disease prevalence, it can lead to interpretation bias and may not accurately reflect real-world settings.
“Additionally, because ROC analysis doesn’t consider disease prevalence, it can lead to interpretation bias and may not accurately reflect real-world settings [198,199].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [199]
ROC curves use the average performance across all thresholds, including those not used in clinical practice, which can limit their clinical relevance.
“However, since ROC curves use the average performance across all thresholds, including those not used in clinical practice, this can limit their clinical relevance.”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [199]
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References 31 · Cited by 255
Research works printed but not linked: 27
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- Authors
- Halligan, S., Altman, D. G., & Mallett, S.
- Affiliations
- University College Hospital (GB)
- University of Oxford (GB)
- Corresponding author
- Halligan, Steve
- Venue
- European Radiology
- Published
- 2015-01-20
- Volume
- 25
- Issue
- 4
- Pages
- 932-939
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- PMC
- PMC4356897
- OpenAlex
- W2072324809
- Cited by
- 255
- References
- 31
- Citations by year
- 2026: 26 · 2025: 24 · 2024: 29 · 2023: 25 · 2022: 37 · 2021: 28 · 2020: 25 · 2019: 14 · 2018: 27 · 2017: 10 · 2016: 7 · 2015: 3
- FWCI
- 13.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Radiology (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Funders
- Cancer Research UK (16895)
- National Institute for Health and Care Research (RP-PG-0407-10338)
- Programme Grants for Applied Research
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- area under the receiver operating characteristic, net benefit, diagnostic performance, CT colonography, computer-aided detection, misclassification cost, prevalence, sensitivity and specificity
- MeSH terms
- Diagnostic Imaging, Humans, ROC Curve, Sensitivity and Specificity, Reproducibility of Results, Area Under Curve, Colonography, Computed Tomographic
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