scholarly journals On the Binormal Predictive Receiver Operating Characteristic Curve for the Joint Assessment of Positive and Negative Predictive Values

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 593 ◽  
Author(s):  
Gareth Hughes
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Statistical Evaluation ◽  
Characteristic Curve ◽  
Disease Status ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Predictive Values ◽  
Receiver Operating

The predictive receiver operating characteristic (PROC) curve is a diagrammatic format with application in the statistical evaluation of probabilistic disease forecasts. The PROC curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for evaluation using metrics defined conditionally on the outcome of the forecast rather than metrics defined conditionally on the actual disease status. Starting from the binormal ROC curve formulation, an overview of some previously published binormal PROC curves is presented in order to place the PROC curve in the context of other methods used in statistical evaluation of probabilistic disease forecasts based on the analysis of predictive values; in particular, the index of separation (PSEP) and the leaf plot. An information theoretic perspective on evaluation is also outlined. Five straightforward recommendations are made with a view to aiding understanding and interpretation of the sometimes-complex patterns generated by PROC curve analysis. The PROC curve and related analyses augment the perspective provided by traditional ROC curve analysis. Here, the binormal ROC model provides the exemplar for investigation of the PROC curve, but potential application extends to analysis based on other distributional models as well as to empirical analysis.

scholarly journals Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 938 ◽  
Author(s):  
Gareth Hughes ◽  
Jennifer Kopetzky ◽  
Neil McRoberts
Keyword(s):  
Mutual Information ◽  
Receiver Operating Characteristic ◽  
Risk Score ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Curve Analysis ◽  
Summary Statistics ◽  
Predictive Values ◽  
Score Threshold ◽  
Receiver Operating

The predictive receiver operating characteristic (PROC) curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for the evaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the test rather than metrics defined conditionally on the actual disease status. Application of PROC curve analysis may be hindered by the complex graphical patterns that are sometimes generated. Here we present an information theoretic analysis that allows concurrent evaluation of PROC curves and ROC curves together in a simple graphical format. The analysis is based on the observation that mutual information may be viewed both as a function of ROC curve summary statistics (sensitivity and specificity) and prevalence, and as a function of predictive values and prevalence. Mutual information calculated from a 2 × 2 prediction-realization table for a specified risk score threshold on an ROC curve is the same as the mutual information calculated at the same risk score threshold on a corresponding PROC curve. Thus, for a given value of prevalence, the risk score threshold that maximizes mutual information is the same on both the ROC curve and the corresponding PROC curve. Phytopathologists and clinicians who have previously relied solely on ROC curve summary statistics when formulating risk thresholds for application in practical agricultural or clinical decision-making contexts are thus presented with a methodology that brings predictive values within the scope of that formulation.

scholarly journals Clinical Characteristics and MRI-based Prediction of the KLF4K409Q-mutation in Meningioma

2021 ◽  
Author(s):  
Niklas von Spreckelsen ◽  
Natalie Waldt ◽  
Marco Timmer ◽  
Lukas Goertz ◽  
David Reinecke ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Tumor Size ◽  
Operating Characteristic ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Small Tumor ◽  
Imaging Characteristics ◽  
Mutational Status ◽  
Receiver Operating

Abstract Purpose: Meningioma is the most common primary brain tumor in adults. In recent years, several non-NF2 mutations, i.e. AKT1, SMO, TRAF7, and KLF4 mutations, specific for meningioma have been identified. This study aims to analyze the clinical impact and imaging characteristics of the KLF4K409Q mutation in meningioma. Methods: Clinical, neuropathological, and imaging data of 170 patients who underwent meningioma resection between 2013 and 2018 were retrospectively collected and tumors were analyzed for the presence of the KLF4K409Q mutation. We collected imaging characteristics, performed semiautomatic volumetric analysis of tumor size and peritumoral edema (PTBE), and calculated the edema index (EI, i.e. ratio of PTBE to tumor volume). Receiver operating characteristic (ROC) curve analysis was performed to identify cut-off EI values to predict the mutational status of KLF4.Results: Eighteen (10.6%) of the meningiomas carried the KLF4K409Qmutation; these were significantly associated with a secretory subtype (p<0.001) and sphenoid wing location (p=0.029). Small tumor size (p=0.007), an increased PTBE (p=0.012), and an increased EI (p=0.001) proved to be significantly associated with the KLF4K409Q mutation. In receiver operating characteristic (ROC) curve analysis, EI predicted the KLF4K409Q mutation with an AUC of 0.728 (p=0.0016). Conclusion: The KLF4K409Q mutation is associated with a distinct small tumor subtype, prone to substantial PTBE. EI is a reliable parameter to predict the KLF4K409Q mutation in meningioma, thus providing a tool for improvement of pre- and perioperative medical management.

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