scholarly journals Inference in receiver operating characteristic surface analysis via a trinormal model‐based testing approach

Stat ◽  
2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Samuel Noll ◽  
Reinhard Furrer ◽  
Benjamin Reiser ◽  
Christos T. Nakas
Keyword(s):  
Receiver Operating Characteristic ◽  
Surface Analysis ◽  
Operating Characteristic ◽  
Characteristic Surface ◽  
Model Based ◽  
Model Based Testing ◽  
Testing Approach ◽  
Receiver Operating

Estimation of the volume under the receiver-operating characteristic surface adjusting for non-ignorable verification bias

2018 ◽  
Vol 27 (3) ◽  
pp. 715-739 ◽  
Author(s):  
Ying Zhang ◽  
Todd A Alonzo ◽  
Keyword(s):  
Receiver Operating Characteristic ◽  
Diagnostic Test ◽  
Operating Characteristic ◽  
Disease Status ◽  
Characteristic Surface ◽  
Finite Sample ◽  
Classification Problems ◽  
Verification Bias ◽  
Inverse Probability Weighted ◽  
Receiver Operating

The receiver-operating characteristic surface is frequently used for presenting the accuracy of a diagnostic test for three-category classification problems. One common problem that can complicate the estimation of the volume under receiver-operating characteristic surface is that not all subjects receive the verification of the true disease status. Estimation based only on data from subjects with verified disease status may be biased, which is referred to as verification bias. In this article, we propose new verification bias correction methods to estimate the volume under receiver-operating characteristic surface for a continuous diagnostic test. We assume the verification process is missing not at random, which means the missingness might be related to unobserved clinical characteristics. Three classes of estimators are proposed, namely, inverse probability weighted, imputation-based, and doubly robust estimators. A jackknife estimator of variance is derived for all the proposed volume under receiver-operating characteristic surface estimators. The finite sample properties of the new estimators are examined via simulation studies. We illustrate our methods with data collected from Alzheimer’s disease research.

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma based on kupffer phase radiomic features of sonazoid contrast-enhanced ultrasound (SCEUS): A prospective study

2022 ◽  
pp. 1-11
Author(s):  
Yi Dong ◽  
Dan Zuo ◽  
Yi-Jie Qiu ◽  
Jia-Ying Cao ◽  
Han-Zhang Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Microvascular Invasion ◽  
Ultrasound Images ◽  
Contrast Enhanced Ultrasound ◽  
Model Based ◽  
Preoperative Prediction ◽  
Contrast Enhanced ◽  
Receiver Operating

OBJECTIVES: To establish and evaluate a machine learning radiomics model based on grayscale and Sonazoid contrast enhanced ultrasound images for the preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. METHODS: 100 cases of histopathological confirmed HCC lesions were prospectively included. Regions of interest were segmented on both grayscale and Kupffer phase of Sonazoid contrast enhanced (CEUS) images. Radiomic features were extracted from tumor region and region containing 5 mm of peritumoral liver tissues. Maximum relevance minimum redundancy (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) were used for feature selection and Support Vector Machine (SVM) classifier was trained for radiomic signature calculation. Radiomic signatures were incorporated with clinical variables using univariate-multivariate logistic regression for the final prediction of MVI. Receiver operating characteristic curves, calibration curves and decision curve analysis were used to evaluate model’s predictive performance of MVI. RESULTS: Age were the only clinical variable significantly associated with MVI. Radiomic signature derived from Kupffer phase images of peritumoral liver tissues (kupfferPT) displayed a significantly better performance with an area under the receiver operating characteristic curve (AUROC) of 0.800 (95% confidence interval: 0.667, 0.834), the final prediction model using Age and kupfferPT achieved an AUROC of 0.804 (95% CI: 0.723, 0.878), accuracy of 75.0%, sensitivity of 87.5% and specificity of 69.1%. CONCLUSIONS: Radiomic model based on Kupffer phase ultrasound images of tissue adjacent to HCC lesions showed an observable better predictive value compared to grayscale images and has potential value to facilitate preoperative identification of HCC patients at higher risk of MVI.

Confidence intervals for differences between volumes under receiver operating characteristic surfaces (VUS) and generalized Youden indices (GYIs)

2017 ◽  
Vol 27 (3) ◽  
pp. 675-688 ◽  
Author(s):  
Jingjing Yin ◽  
Christos T Nakas ◽  
Lili Tian ◽  
Benjamin Reiser
Keyword(s):  
Cognitive Function ◽  
Receiver Operating Characteristic ◽  
Confidence Intervals ◽  
Operating Characteristic ◽  
Youden Index ◽  
Characteristic Surface ◽  
Classification Problems ◽  
New Methods ◽  
Using Data ◽  
Receiver Operating

This article explores both existing and new methods for the construction of confidence intervals for differences of indices of diagnostic accuracy of competing pairs of biomarkers in three-class classification problems and fills the methodological gaps for both parametric and non-parametric approaches in the receiver operating characteristic surface framework. The most widely used such indices are the volume under the receiver operating characteristic surface and the generalized Youden index. We describe implementation of all methods and offer insight regarding the appropriateness of their use through a large simulation study with different distributional and sample size scenarios. Methods are illustrated using data from the Alzheimer's Disease Neuroimaging Initiative study, where assessment of cognitive function naturally results in a three-class classification setting.

Prenatal diagnosis of CHDs: a simple ultrasound prediction model to estimate the probability of the need for neonatal cardiac invasive therapy

2015 ◽  
Vol 26 (2) ◽  
pp. 347-353 ◽  
Author(s):  
Pablo Marantz ◽  
Sofía Grinenco ◽  
Fabio Pestchanker ◽  
César H. Meller ◽  
Gustavo Izbizky
Keyword(s):  
Prenatal Diagnosis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Accurate Estimation ◽  
Invasive Therapy ◽  
Model Based ◽  
Echocardiographic Findings ◽  
Receiver Operating

AbstractObjectivesTo develop a prediction model based on echocardiographic findings to estimate the probability of the need for neonatal cardiac invasive therapy, including cardiac surgery or catheter-based therapy, in foetuses with CHD.MethodsRetrospective cohort study: a prediction model was developed based on echocardiographic findings on the examination of the four-chamber, the three-vessel, and the three-vessel and tracheal views. We assessed performance using the area under the curve of the receiver operating characteristic.ResultsAmong 291 patients with prenatal diagnosis of CHD and complete follow-up, 175 (60.1%) required neonatal cardiac invasive therapy. The variables “functionally single ventricle”, “great artery reverse flow”, and “congenital heart block” had a discrimination value of 100% and were excluded from the model. In univariate and multivariate analysis, “non-visualisation of a great vessel”, “asymmetry of the great vessels”, “visualisation of one atrioventricular valve”, and “ventricular asymmetry” were significantly associated with the need for neonatal cardiac invasive therapy. The area under the receiver operating characteristic curve was 0.9324 (95% CI 0.92–0.97).ConclusionsA prediction model based on echocardiographic findings in foetuses with CHD, even without a definite diagnosis, allows an accurate estimation of the probability of requiring neonatal cardiac invasive therapy. This can modify patient care, especially in regions where a Foetal Medicine Specialist or a Paediatric Cardiologist is not available and referral may be extremely difficult due to social and economic barriers.

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