Receiver operating characteristic (ROC) analysis of images reconstructed with iterative expectation maximization algorithms

2001 ◽  
Vol 15 (6) ◽  
pp. 521-525 ◽  
Author(s):  
Yasuyuki Takahashi ◽  
Kenya Murase ◽  
Hiroshi Higashino ◽  
Ichiro Sogabe ◽  
Kana Sakamoto
Keyword(s):  
Receiver Operating Characteristic ◽  
Expectation Maximization ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Expectation Maximization Algorithms ◽  
Maximization Algorithms ◽  
Receiver Operating

scholarly journals Interaction of MRE11 and Clinicopathologic Characteristics in Recurrence of Breast Cancer: Individual and Cumulated Receiver Operating Characteristic Analyses

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Sin-Hua Moi ◽  
Li-Yeh Chuang ◽  
Shyng-Shiou F. Yuan ◽  
Ming-Feng Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Growth Factor Receptor ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Prognostic Indicators ◽  
Clinicopathologic Characteristics ◽  
Receiver Operating

The interaction between the meiotic recombination 11 homolog A (MRE11) oncoprotein and breast cancer recurrence status remains unclear. The aim of this study was to assess the interaction between MRE11 and clinicopathologic variables in breast cancer. A dataset for 254 subjects with breast cancer (220 nonrecurrent and 34 recurrent) was used in individual and cumulated receiver operating characteristic (ROC) analyses of MRE11 and 12 clinicopathologic variables for predicting breast cancer recurrence. In individual ROC analysis, the area under curve (AUC) for each predictor of breast cancer recurrence was smaller than 0.7. In cumulated ROC analysis, however, the AUC value for each predictor improved. Ten relevant variables in breast cancer recurrence were used to find the optimal prognostic indicators. The presence of any six of the following ten variables had a high (79%) sensitivity and a high (70%) specificity for predicting breast cancer recurrence: tumor size ≥ 2.4 cm, tumor stage II/III, therapy other than hormone therapy, age ≥ 52 years, MRE11 positive cells > 50%, body mass index ≥ 24, lymph node metastasis, positivity for progesterone receptor, positivity for epidermal growth factor receptor, and negativity for estrogen receptor. In conclusion, this study revealed that these 10 clinicopathologic variables are the minimum discriminators needed for optimal discriminant effectiveness in predicting breast cancer recurrence.

scholarly journals Bayesian Sensitivity-Specificity and ROC Analysis for Finding Key Drivers

2021 ◽  
Vol 19 (1) ◽  
pp. 2-15
Author(s):  
Stan Lipovetsky ◽  
Michael W. Conklin
Keyword(s):  
Receiver Operating Characteristic ◽  
Statistical Modeling ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Regression Modeling ◽  
Modeling And Analysis ◽  
Numerical Comparisons ◽  
Key Drivers ◽  
Sensitivity Specificity ◽  
Receiver Operating

Finding key drivers in regression modeling via Bayesian Sensitivity-Specificity and Receiver Operating Characteristic is suggested, and clearly interpretable results are obtained. Numerical comparisons with other techniques show that this methodology can be useful in practical statistical modeling and analysis helping to researchers and managers in making meaningful decisions.

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.

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