Receiver operating characteristic curves and their use in radiology.

By Nancy A. Obuchowski. Radiology, 2003

879 citations recorded by the source corpus.

Subfields: Epidemiology, Physiology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ad40a318701eca3eab9e9dc7bfea20d1811a966a

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Citations in the corpus

Assessing the performance of prediction models: a framework for some traditional and novel measures (2010, background): “Discrimination can be quantified with measures such as sensitivity, specificity, and the area under the receiver operating characteristic curve (or concordance statistic, c).(1,5) Recently, several new measures have been proposed to assess performance of a prediction model.”
ROC analysis: applications to the classification of biological sequences and 3D structures (2008, background): “The partial area under the ROC curve (pAUC) [34] is defined as the area between two FPRs, FPR1 and FPR2, and can be denoted as A(FPR1 FPR FPR2) [35] (Figure 6, inset B).”
What's under the ROC? An Introduction to Receiver Operating Characteristics Curves (2007, methodology): “Even if the AUCs aren’t the same, but the lines cross, it’s necessary to choose between them by using a finer-grained test, called the partial AUC, or pAUC.(9,13) Rather than calculating the AUC over the entire range of the test, we focus our attention on that portion of curves that is of most inter”
Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments (2018, methodology): “…we constructed receiver operating characteristic (ROC) curves, which evaluate the performance of a model in a way that takes the uncertainty of each prediction into account [see the study of Obuchowski ( 22 ) for details on use of ROC curvesforassessingperformanceofbinaryclassificationmodels].”
Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach (2014, methodology): “These networks are a new powerful tool for approximation and deals effectively with the problems of high dimensional model [31].”
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance (2008, methodology): “To account for these issues, Receiver Operator Characteristic (ROC) analysis is commonly used in the clinical CAD community (Obuchowski, 2003).”
Receiver operating characteristic curve: overview and practical use for clinicians (2022, background): “By expanding the sample size and connecting countless points, the parametric ROC curve forms the shape of a smooth curve [10].”
Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. (2013, neutral): “ROC curve analysis has several advantages (31-36).”
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders (2013, methodology): “The mean AUC, as an average performance, gives an indication of a typical AUC obtained using the given input data, and indicates how reliably result is estimated [52–55].”
Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists (2004, neutral): “14 Korean J Radiol 5(1), March 2004 Fig.”
The use of receiver operating characteristic curves in biomedical informatics (2005, background): “For a review focusing on ordinal data, see [3].”