ArticleEuropean radiology2015
Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: a discussion and proposal for an alternative approach.
Article in European radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
116 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Diabetic Foot Ulcer Classification Models Using Artificial Intelligence and Machine Learning Techniques: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis.European radiology · 2024Pooled it
- EEG Patterns and Outcomes After Hypoxic Brain Injury: A Systematic Review and Meta-analysis.Neurocritical care · 2022Pooled it
- Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? A systematic review.BMC medical informatics and decision making · 2022Pooled it
- Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis.Neuroradiology · 2021Pooled it
- Comparing mortality risk models in VLBW and preterm infants: systematic review and meta-analysis.Journal of perinatology : official journal of the California Perinatal Association · 2020Pooled it
- SIRS or qSOFA? Is that the question? Clinical and methodological observations from a meta-analysis and critical review on the prognostication of patients with suspected sepsis outside the ICU.Internal and emergency medicine · 2019Pooled it
- Trial
- Influence of red blood cell indices on HbA1c performance in detecting dysglycaemia in a Singapore preconception cohort study.Scientific reports · 2021Trial
- Integrating Clinical Priorities into the Technical Validation of Machine Learning Classifiers: A Generalizable Framework Demonstrated on Opioid Misuse Screening.Journal of clinical medicine · 2026Article
- CT-Based Peritumoral and Perirenal Fat Radiomics in Renal Cell Carcinoma: A Systematic Review and Meta-Analysis of Grade, Stage, and Adherent Perinephric Fat Prediction.Journal of clinical medicine · 2026Review
- Loss function influence on hyperparameter optimization for observational healthcare prediction models.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Biomarkers in Clinical Medicine Research: A Literature Survey in the PubMed Database and a Critical Evaluation.Journal of clinical medicine · 2026Review
- Review
- Exploring the Association of Systolic Blood Pressure and Intracranial Pressure Variability and Subarachnoid Hemorrhage Patient Outcomes.Journal of clinical medicine · 2026Article
- MetaXVP: an interpretable machine learning framework for deep insight into variant pathogenicity and VUS classification.Scientific reports · 2026Article
- Trial emulation for validating the clinical efficacy of a foundational AI model in embryo selection.NPJ digital medicine · 2026Article
- Research on Threshold Optimization and Variability-Based Digital Biomarker Approaches Through MMSE-Lifelog Multimodal Integrated Analysis from a Clinical Screening Perspective.Healthcare (Basel, Switzerland) · 2026Article
- Rethinking Diagnostic Performance Metrics in Imaging Artificial Intelligence: Lessons from Caries Detection on Radiographs.Caries research · 2026Review
- Performance of breast cancer risk prediction algorithms across mammography systems in the UK screening programme.NPJ digital medicine · 2026Article
56 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectivesThe 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.
methodsWe use a narrative review supplemented by data from a study of computer-assisted detection for CT colonography.
resultsWe 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.
conclusionsROC 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.