scholarly journals Receiver operating characteristic analysis. Application to the study of quantum fluctuation effects in optic nerve of Rana pipiens.

1975 ◽  
Vol 66 (5) ◽  
pp. 583-616 ◽  
Author(s):  
T E Cohn ◽  
D G Green ◽  
W P Tanner
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Quantum Fluctuation ◽  
Ganglion Cells ◽  
Roc Curves ◽  
Characteristic Analysis ◽  
Luminance Change ◽  
Analysis Application ◽  
Receiver Operating

Receiver operating characteristic (ROC) analysis of nerve messages is described. The hypothesis that quantum fluctuations provide the only limit to the ability of frog ganglion cells to signal luminance change information is examined using ROC analysis. In the context of ROC analysis, the quantum fluctuation hypothesis predicts (a) the detectability of a luminance change signal should rise proportionally to the size of the change, (b) detectability should decrease as the square root of background, an implication of which is the deVries-Rose law, and (c) ROC curves should exhibit a shape particular to underlying Poisson distributions. Each of these predictions is confirmed for the responses of dimming ganglion cells to brief luminance decrements at scotopic levels, but none could have been tested using classical nerve message analysis procedures.

Receiver operating characteristic analysis of milk lactosefor identification of mastitis in buffaloes

2016 ◽  
Author(s):  
T. K. Patbandha ◽  
K. Ravikala ◽  
B. R. Maharana ◽  
Rupal Pathak ◽  
S. Marandi ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Positive Likelihood Ratio ◽  
Threshold Values ◽  
Characteristic Analysis ◽  
Statistical Tool ◽  
Milk Samples ◽  
Diagnostic Indicator ◽  
Receiver Operating

Receiver operating characteristic (ROC) analysis is a simple statistical tool used to classify a diagnostic indicator in terms of area under a ROC curve (AUC) and to develop potential threshold values of a diagnostic indicator. Milk lactose was analyzed by ROC analysis to see its accuracy to discriminate infected and healthy udder quarters, and to develope an optimum threshold value along with corresponding sensitivity (Se), specificity (Sp) and positive likelihood ratio (LR+) value. Data for the present study comprised of 1516 milk samples collected from Jaffrabadi buffaloes. Milk lactose was estimated by milk analyzer ‘LACTOSCAN’ and further samples were checked for sub-clinical mastitis by California mastitis test (CMT). The threshold values of milk lactose for identification of moderate and severe infection were found to be 5.31g% (Se, 58.82%; Sp, 58.28%) and 5.23g% (Se, 70.97%; Sp, 64.41%), respectively by ROC analysis. Milk samples with lactose content below 5.31g% were 1.41 times more likely come from moderately infected quarters (LR+ = 1.41); whereas, below 5.23g% were 1.99 times more likely come from severely infected quarters (LR+ = 1.99). The overall accuracy of milk lactose for discrimination of normal quarters from moderately infected quarters was 64% (AUC=0.64) and from severely infected quarters was 72% (AUC=0.72) (P<0.001). Thus, the present study indicated that milk lactose classified mastitic and healthy udder quarters in Jaffrabadi buffaloes with moderate accuracy.

Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information-Based Decision-Making

2019 ◽  
pp. 764-776 ◽  
Author(s):  
Nan Hu
Keyword(s):  
Decision Making ◽  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Decision Rules ◽  
Scoring Systems ◽  
Roc Curves ◽  
Decision Tools ◽  
Classification Tool ◽  
Receiver Operating

Business operators and stakeholders often need to make decisions such as choosing between A and B, or between yes and no, and these decisions are often made by using a classification tool or a set of decision rules. Decision tools usually include scoring systems, predictive models, and quantitative test modalities. In this chapter, the authors introduce the receiver operating characteristic (ROC) curves and demonstrate, through an example of bank decision on granting loans to customers, how ROC curves can be used to evaluate decision making for information-based decision making. In addition, an extension to time-dependent ROC analysis is introduced in this chapter. The authors conclude this chapter by illustrating the application of ROC analysis in information-based decision making and providing the future trends of this topic.

Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information -Based Decision-Making

2018 ◽  
pp. 2213-2223
Author(s):  
Nan Hu
Keyword(s):  
Decision Making ◽  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Decision Rules ◽  
Scoring Systems ◽  
Roc Curves ◽  
Decision Tools ◽  
Classification Tool ◽  
Receiver Operating

Business operators and stakeholders often need to make decisions such as choosing between A and B, or between yes and no, and these decisions are often made by using a classification tool or a set of decision rules. Decision tools usually include scoring systems, predictive models, and quantitative test modalities. In this chapter, we introduce the receiver operating characteristic (ROC) curves and demonstrate, through an example of bank decision on granting loans to customers, how ROC curves can be used to evaluate decision making for information based decision making. In addition, an extension to time-dependent ROC analysis is introduced in this chapter. We conclude this chapter by illustrating the application of ROC analysis in information based decision making and providing the future trends of this topic.

Use of Receiver Operating Characteristic (ROC) Curves to Evaluate Computer Confidence Threshold and Clinical Performance in the Diagnosis of Appendicitis

1978 ◽  
Vol 17 (03) ◽  
pp. 157-161 ◽  
Author(s):  
F. T. De Dombal ◽  
Jane C. Horrocks
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Clinical Performance ◽  
Roc Curves ◽  
Final Diagnosis ◽  
Confidence Threshold ◽  
Computer Aided ◽  
Computer Confidence ◽  
Overall Performance ◽  
Receiver Operating

This paper uses simple receiver operating characteristic (ROC) curves (i) to study the effect of varying computer confidence of threshold levels and (ii) to evaluate clinical performance in the diagnosis of acute appendicitis. Over 1300 patients presenting to five centres with abdominal pain of short duration were studied in varying detail. Clinical and computer-aided diagnostic predictions were compared with the »final« diagnosis. From these studies it is concluded the simplistic setting of a 50/50 confidence threshold for the computer program is as »good« as any other. The proximity of a computer-aided system changed clinical behaviour patterns; a higher overall performance level was achieved and clinicians performance levels became associated with the »mildly conservative« end of the computers ROC curve. Prior forecasts of over-confidence or ultra-caution amongst clinicians using the computer-aided system have not been fulfilled.

scholarly journals MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 949
Author(s):  
Cecil J. Weale ◽  
Don M. Matshazi ◽  
Saarah F. G. Davids ◽  
Shanel Raghubeer ◽  
Rajiv T. Erasmus ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Tolerance Test ◽  
Cross Sectional Study ◽  
Oral Glucose ◽  
Cross Sectional ◽  
Receiver Operating

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies.

scholarly journals 419. Diagnostic Utility of a Ferritin to Procalcitonin Ratio to Differentiate Patients with COVID-19 from Those with Bacterial Pneumonia

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S277-S277
Author(s):  
Katherine C Jankousky ◽  
Peter Hyson ◽  
Jin Huang ◽  
Daniel B Chastain ◽  
Carlos Franco-Paredes ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Host Response ◽  
Bacterial Pneumonia ◽  
Operating Characteristic ◽  
Receiver Operating Characteristic Analysis ◽  
University Hospital ◽  
Characteristic Analysis ◽  
The Us ◽  
Test Probability ◽  
Receiver Operating

Abstract Background Accurate, rapid, inexpensive biomarkers are needed to differentiate COVID-19 from bacterial pneumonia, allowing effective treatment and antibiotic stewardship. We hypothesized that the ratio of ferritin to procalcitonin (F/P) reflects greater viral activity and host response with COVID-19 pneumonia, while bacterial pneumonia would be associated with less cytolysis (lower ferritin) and more inflammation (higher procalcitonin), thus a lower F/P ratio. Methods We conducted a retrospective study of adult patients admitted to a single University hospital in the US through May 2020, during the COVID-19 pandemic. We compared F/P ratio of patients diagnosed with COVID-19 or bacterial pneumonia, excluding patients with COVID-19 and bacterial co-infections. In a logistic regression, we controlled for age, sex, body mass index (BMI), diabetes (DM), and hypertension (HTN). We used a receiver operating characteristic analysis to calculate the sensitivity and specificity of F/P values for the diagnosis of COVID-19 versus bacterial pneumonia. Results Of 218 patients with COVID-19 and 17 with bacterial pneumonia, COVID-19 patients were younger (56 vs 66 years, p=0.04), male (66% vs 24%, p=0.009), had higher BMI (31 vs 27 kg/m2, p=0.03), and similar rates of HTN (59% vs 45%, p=0.3) and DM (32% vs 18%, p=0.2). The median F/P ratio was significantly higher in patients with COVID-19 (3195 vs 860, p=0.0003, Figure 1). An F/P ratio cut-off of ≥ 1250 generated a sensitivity of 78% and a specificity of 59% to correctly classify a COVID-19 case (Figure 2). When adjusted for age, gender, BMI, DM, and HTN, a ratio ≥ of 1250 was associated with significantly greater odds of COVID-19 versus bacterial pneumonia (OR: 4.9, CI: 1.5, 16.1, p=0.009). Figure 1. Ferritin to Procalcitonin Ratios of patients with COVID-19 and patients with Bacterial Pneumonia (controls). Figure 2. Receiver Operating Characteristic Analysis of Ferritin to Procalcitonin Ratio Cut-off Values Predicting COVID-19 Diagnosis. Conclusion We observed an elevated F/P ratio in patients with COVID-19 compared to those with bacterial pneumonia. A F/P ratio ≥ 1250 provides a clinically relevant increase in pre-test probability of COVID-19. Prospective studies evaluating the discriminatory characteristics of F/P ratio in larger cohorts is warranted. Disclosures All Authors: No reported disclosures

scholarly journals Lesion-to-background ratio threshold value of SUVmax of simultaneous [68Ga]Ga-PSMA-11 PET/MRI imaging in patients with prostate cancer

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jing Zhao ◽  
Bernd Hamm ◽  
Winfried Brenner ◽  
Marcus R. Makowski
Keyword(s):  
Prostate Cancer ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Absolute Threshold ◽  
Threshold Value ◽  
Imaging Findings ◽  
Characteristic Analysis ◽  
Psma Pet ◽  
The Absolute ◽  
Receiver Operating

Abstract Purpose This study aimed to calculate an applicable relative ratio threshold value instead of the absolute threshold value for simultaneous 68Ga prostate-specific membrane antigen/positron emission tomography ([68Ga]Ga-PSMA-11 PET) in patients with prostate cancer (PCa). Materials and methods Our study evaluated thirty-two patients and 170 focal prostate lesions. Lesions are classified into groups according to Prostate Imaging Reporting and Data System (PI-RADS). Standardized uptake values maximum (SUVmax), corresponding lesion-to-background ratios (LBRs) of SUVmax, and LBR distributions of each group were measured based on regions of interest (ROI). We examined LBR with receiver operating characteristic analysis to determine threshold values for differentiation between multiparametric magnetic resonance imaging (mpMRI)-positive and mpMRI-negative lesions. Results We analyzed a total of 170 focal prostate lesions. Lesions number of PI-RADS 2 to 5 was 70, 16, 46, and 38. LBR of SUVmax of each PI-RADS scores was 1.5 (0.9, 2.4), 2.5 (1.6, 3.4), 3.7 (2.6, 4.8), and 6.7 (3.5, 12.7). Based on an optimal threshold ratio of 2.5 to be exceeded, lesions could be classified into MRI-positive lesion on [68Ga]Ga-PSMA PET with a sensitivity of 85.2%, a specificity of 72.0%, with the corresponding area under the receiver operating characteristic curve (AUC) of 0.83, p < 0.001. This value matches the imaging findings better. Conclusion The ratio threshold value of SUVmax, LBR, has improved clinical and research applicability compared with the absolute value of SUVmax. A higher threshold value than the background’s uptake can dovetail the imaging findings on MRI better. It reduces the bias from using absolute background uptake value as the threshold value.

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