scholarly journals Fully automated waist circumference measurement on abdominal CT: Comparison with manual measurements and potential value for identifying overweight and obesity as an adjunct output of CT scan

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254704
Author(s):  
Ijin Joo ◽  
Min-Sun Kwak ◽  
Dae Hyun Park ◽  
Soon Ho Yoon
Keyword(s):  
Waist Circumference ◽  
Roc Curve ◽  
Pearson Correlation ◽  
Overweight And Obesity ◽  
Segmentation Algorithm ◽  
Ct Scans ◽  
Operating Characteristics ◽  
Abdominal Ct ◽  
Anthropometric Parameter ◽  
Body Segmentation

Objective Waist circumference (WC) is a widely accepted anthropometric parameter of central obesity. We investigated a fully automated body segmentation algorithm for measuring WC on abdominal computed tomography (CT) in comparison to manual WC measurements (WC-manual) and evaluated the performance of CT-measured WC for identifying overweight/obesity. Materials and methods This retrospective study included consecutive adults who underwent both abdominal CT scans and manual WC measurements at a health check-up between January 2013 and November 2019. Mid-waist WCs were automatically measured on noncontrast axial CT images using a deep learning-based body segmentation algorithm. The associations between CT-measured WC and WC-manual was assessed by Pearson correlation analysis and their agreement was assessed through Bland-Altman analysis. The performance of these WC measurements for identifying overweight/obesity (i.e., body mass index [BMI] ≥25 kg/m2) was evaluated using receiver operating characteristics (ROC) curve analysis. Results Among 763 subjects whose abdominal CT scans were analyzed using a fully automated body segmentation algorithm, CT-measured WCs were successfully obtained in 757 adults (326 women; mean age, 54.3 years; 64 women and 182 men with overweight/obesity). CT-measured WC was strongly correlated with WC-manual (r = 0.919, p < 0.001), and showed a mean difference of 6.1 cm with limits of agreement between -1.8 cm and 14.0 cm in comparison to WC-manual. For identifying overweight/obesity, CT-measured WC showed excellent performance, with areas under the ROC curve (AUCs) of 0.960 (95% CI, 0.933–0.979) in women and 0.909 (95% CI, 0.878–0.935) in men, which were comparable to WC-manual (AUCs of 0.965 [95% CI, 0.938–0.982] and 0.916 [95% CI, 0.886–0.941]; p = 0.735 and 0.437, respectively). Conclusion CT-measured WC using a fully automated body segmentation algorithm was closely correlated with manually-measured WC. While radiation issue may limit its general use, it can serve as an adjunctive output of abdominal CT scans to identify overweight/obesity.

scholarly journals Sensitivity and specificity of waist circumference as a single screening tool for assessment of overweight and obesity

2017 ◽  
Vol 4 (11) ◽  
pp. 4254
Author(s):  
Anubrata Karmakar ◽  
Shobhit Garg ◽  
Aparajita Dasgupta ◽  
Bobby Paul ◽  
Swanya P. Maharana
Keyword(s):  
Waist Circumference ◽  
Sensitivity And Specificity ◽  
Roc Curve ◽  
Screening Tool ◽  
Cross Sectional Study ◽  
Overweight And Obesity ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Public Health Perspective ◽  
Cross Sectional

Background: Generalised and central obesity are established risk factors for metabolic syndrome and cardiovascular diseases. Easy assessment of overweight or obesity is the need of the hour from public health perspective. Waist circumference (WC) can be a simple screening tool for identifying overweight individuals since measuring WC is simple, inexpensive, less time consuming, convenient for self-monitoring and needs no complicated calculation as BMI.Methods: A community based cross-sectional study was conducted in January-February 2017 among 338 adults, in a village of Singur Block, West Bengal. Height, weight and WC were measured for each subject. Receiver Operating Characteristic (ROC) curve analysis was used to estimate the cut-off values of WC.Results: The sensitivity and specificity of WC ≥90 centimeters for men for identifying overweight (BMI ≥25) were 78.8% and 75.6% respectively, whereas those of WC ≥80 cm for women were 80.3% and 44% respectively. ROC curve analysis revealed good diagnostic accuracy at 88.5 cm for WC cut-off for men (area under curve (AUC) 0.854, sensitivity 86.5%, specificity 67.6%) and fair accuracy (AUC 0.744, sensitivity 80.3%, specificity 44%) for WC cut-off for 80 cm for women.Conclusions: This study shows, WC can be used for screening of overweight individual infield practice as measuring tape is inexpensive and easy-to-carry compared to a weighing scale. More research may be done on larger sample size to establish an optimal WC cut-off value for Indian population. 

scholarly journals What are the optimal cut-off points of anthropometric indices for prediction of overweight and obesity? Predictive validity of waist circumference, waist-to-hip and waist-to-height ratios

2020 ◽  
Vol 10 (2) ◽  
pp. 142-147 ◽  
Author(s):  
Helda Tutunchi ◽  
Mehrangiz Ebrahimi-Mameghani ◽  
Alireza Ostadrahimi ◽  
Mohammad Asghari-Jafarabadi
Keyword(s):  
Waist Circumference ◽  
Roc Curve ◽  
Predictive Ability ◽  
Diagnostic Value ◽  
Cross Sectional Study ◽  
Overweight And Obesity ◽  
Roc Curve Analysis ◽  
Anthropometric Indices ◽  
Cross Sectional ◽  
Anthropometric Indicators

Background: Planning for obesity prevention is an important global health priority. Our aim in this study was to find the optimal cut-off points of waist circumference (WC), waist- to- hipratio (WHR) and waist- to- height ratio (WHtR), as three anthropometric indices, for prediction of overweight and obesity. We also aimed to compare the predictive ability of these indices to introduce the best choice. Methods: In this cross-sectional study, a total of 500 subjects were investigated. Anthropometric indicators were measured using a standard protocol. We considered body mass index (BMI) as the simple and most commonly used index for measuring general obesity as the comparison indicator in the present study to assess the diagnostic value for other reported obesity indices.We also performed receiver operating characteristic (ROC) curve analysis to define the optimal cut-off points of the anthropometric indicators and the best indices for overweight and obesity. Results: The proposed optimal cut-offs for WC, WHtR, and WHR were 84 cm, 0.48 and 0.78for women and 98 cm, 0.56 and 0.87 for men, respectively. The area under the ROC curve ofWHtR (women: AUC=0.97, 95% CI: 0.96-0.99 vs. men: AUC=0.97, 95%CI: 0.96-0.99) and WC(women: AUC=0.97, 95% CI, 0.95-0.99 vs. men: AUC=0.98, 95% CI: 0.97-0.99) were greater than WHR (women: AUC=0.79, 95% CI =0.74-0.85 vs. men: AUC=0.84, 95% CI=0.79-0.88). Conclusion: This study demonstrated that the WC and WHtR indicators are stronger indicators compared to the others. However, further studies using desirable and also local cutoffs against more accurate techniques for body fat measurement such as computerized tumor (CT) scans and dual-energy x-ray absorptiometry (DEXA) are required.

scholarly journals Neck circumference as a discriminator of obesity in older women enrolled in a physical activity program

2017 ◽  
Vol 19 (6) ◽  
pp. 710-719
Author(s):  
Daniel Rogério Petreça ◽  
Enaiane Cristina Menezes ◽  
Paula Fabricio Sandreschi ◽  
Felipe Fank ◽  
Giovana Zarpellon Mazo
Keyword(s):  
Physical Activity ◽  
Older Women ◽  
Roc Curve ◽  
Roc Curves ◽  
Overweight And Obesity ◽  
Neck Circumference ◽  
The Body ◽  
Anthropometric Measures ◽  
Anthropometric Parameter ◽  
Physically Active

The aim of this study was to evaluate neck circumference (NC) as a discriminator of overweight and obesity and to establish cut-off points for physically active older women. The sample consisted of 170 older women (69.5 ± 6.8 years) practicing physical activity. Anthropometric measures (body weight, height, waist circumference – WC, and NC) were obtained and the body mass index (BMI) was calculated. Correlation analysis was performed and ROC curves were constructed. NC was significantly correlated with BMI (rho = 0.656; p<0.0001) and WC (r = 0.561; p<0.0001). Correlating BMI with NC, areas under the ROC curve of 0.819 (p=0.0001) for overweight and of 0.902 (p=0.0001) for obesity were obtained, with suggested cut-off points of 33.07 and 34.05 cm, respectively. Correlating WC with NC, areas under the ROC curve of 0.711 (p=0.0014) for moderate risk (WC) and of 0.864 (p=0.0001) for high risk were obtained, with suggested cut-off points of 32.15 and 34.15 cm, respectively. NC was associated with BMI and WC. An NC ≥ 34 cm was a risk factor for obesity and abnormal body fat distribution in the older women studied. This anthropometric parameter is an alternative to discriminate overweight and obesity in physically active older women.

Abstract 14854: Optimal Cut-off Points of Waist Circumference for Detecting Central Obesity and Severe Central Obesity in Chinese Adults

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Xiaoxiao Wen ◽  
Jinzhuang Mai ◽  
Xiangmin Gao ◽  
Min Guo ◽  
Yong Wu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Waist Circumference ◽  
Roc Curve ◽  
Central Obesity ◽  
Adult Population ◽  
Overweight And Obesity ◽  
Chinese Adults ◽  
Chinese Adult ◽  
Increased Risk ◽  
Left Corner

Introduction: Given that no consensus has yet been reached over the optimal cut-off points of waist circumference (WC) for Chinese, this study aimed to determine the appropriate cut-off points of WC for detecting central obesity and severe central obesity in a Chinese adult population. Methods: Data from the cross-sectional survey of the PRC-USA Collaborative study of Cardiovascular and Cardiopulmonary Epidemiology in 1993-1994 was used, including 10265 subjects (4921 men and 5344 women) aged 35-69 years. Each integer of WC in centimeters was used as the cut-off point to detect clustering of cardiovascular risk factors, which was defined as the presence of two or more risk factors among hypertension, diabetes, hypercholesterolemia, hypertriglyceridemia and low levels of high-density lipoprotein cholesterol. Based on the Receiver operating characteristic (ROC) curve analysis, the WC value corresponding to the point on the ROC curve nearest to the upper left corner of the ROC graph was considered the optimal cut-off for central obesity and the value corresponding to the point with specificity of 90% or more was considered the optimal cut-off for severe central obesity. Results: The optimal cut-off value of WC to detect clustering of risk factors was ≥84 cm for men and was ≥80 cm for women with the shortest distance to the upper left corner being 0.4304 and 0.4504, respectively. The cut-off values of WC with specificity above 90% were ≥93 cm and ≥91 cm for men and women respectively. Conclusions: These results were equal or similar to the WC cut-off points proposed by the Guidelines for Prevention and Control of Overweight and Obesity in Chinese Adults, i.e., ≥85/80 cm (men/women) for increased risk for obesity related diseases and ≥95/90 cm (men/women) for much higher risk. For practical reasons, WC≥85/80 cm and ≥95/90 cm (men/women) could be the optimal cut-off values for detecting central obesity and severe central obesity, respectively, in Chinese adult population.

scholarly journals Waist Circumference, Waist Hip Ratio and Body Mass Index in Female Undergraduates of a Tertiary Institution in Nigeria: a Cross-sectional Study

2019 ◽  
Author(s):  
Adamu Rufa'i ◽  
Karimah Sajoh ◽  
Adewale Oyeyemi ◽  
Abdullahi Gwani
Keyword(s):  
Body Mass Index ◽  
Waist Circumference ◽  
Body Mass ◽  
Pearson Correlation ◽  
Overweight And Obesity ◽  
Cross Sectional ◽  
Waist To Hip Ratio ◽  
Prevalence Of Obesity ◽  
Nigerian University ◽  
The Relationship

Purpose: Obesity and overweight are associated with variety of conditions detrimental to health, wellbeing and longevity. Waist circumference and waist to hip ratio are indicators of risk of central adiposity while body mass index is an indicator of overall risk of obesity. Body mass index has been traditionally used as a standard for determining overweight and obesity. This study was designed to determine the relationship between waist circumference, waist to hip ratio and body mass index among female undergraduates of a Nigerian University. Also prevalence of obesity based on waist circumference, waist to hip ratio and body mass index was explored. Methods: Three hundred and sixty four apparently healthy subjects were recruited for the study using a cross-sectional simple random sampling technique. Waist circumference, waist to hip ratio and body mass index were determined using standard methods. Descriptive statistics were used to summarize the physical characteristics of the participants. Pearson correlation coefficient was used to analyze the relationship between waist circumference, waist to hip and body mass index. Results: The mean age, waist circumference, waist to hip ratio and body mass index of the participants were 22.5 (±2.20) years, 79.36 (±10.4) cm, 0.81 (±0.06), and 22.48 (±4.50) kg/m2 respectively. The prevalence of obesity based on body mass index, waist circumference and waist to hip ratio was found to be 6.3%, 17.6% and 25.5% respectively. Significant relationship was found between waist circumference and body mass index (r = 0.81; p< 0.001), and between waist to hip ratio and body mass index (r = 0.25; p< 0.001). Conclusions: Body mass index was related to waist circumference, as well as to waist to hip ratio. The prevalence of obesity based on waist to hip ratio was highest among female undergraduates in a Nigerian university. Awareness on the importance of waist to hip ratio as indicator of risk of obesity should be created among female undergraduates in Nigerian Universities and by extension among the women population in general.

scholarly journals Diagnostic Ability of Peguero-Lo Presti Electrocardiographic Left Ventricular Hypertrophy Criterion in Severe Aortic Stenosis

2021 ◽  
Vol 10 (13) ◽  
pp. 2864
Author(s):  
Aleksandra Gamrat ◽  
Katarzyna Trojanowicz ◽  
Michał A. Surdacki ◽  
Aleksandra Budkiewicz ◽  
Adrianna Wąsińska ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Aortic Stenosis ◽  
Roc Curve ◽  
Ventricular Hypertrophy ◽  
Severe Aortic Stenosis ◽  
Positive Likelihood Ratio ◽  
Left Ventricular ◽  
The Novel ◽  
Operating Characteristics ◽  
Diagnostic Ability

Traditional electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH), introduced in the pre-echocardiographic era of diagnosis, have a relatively low sensitivity (usually not exceeding 25–40%) in detecting LVH. A novel Peguero-Lo Presti ECG-LVH criterion was recently shown to exhibit a higher sensitivity than the traditional ECG-LVH criteria in hypertension. Our aim was to test the diagnostic ability of the novel Peguero-Lo Presti ECG-LVH criterion in severe aortic stenosis. We retrospectively analyzed 12-lead ECG tracings and echocardiographic records from the index hospitalization of 50 patients with isolated severe aortic stenosis (mean age: 77 ± 10 years; 30 women and 20 men). Exclusion criteria included QRS > 120 ms, bundle branch blocks or left anterior fascicular block, a history of myocardial infarction, more than mild aortic or mitral regurgitation, and significant LV dysfunction by echocardiography. We compared the agreement of the novel Peguero-Lo Presti criterion and traditional ECG-LVH criteria with echocardiographic LVH (LV mass index > 95 g/m2 in women and >115 g/m2 in men). Echocardiographic LVH was found in 32 out of 50 study patients. The sensitivity of the Peguero-Lo Presti criterion in detecting LVH was improved (55% vs. 9–34%) at lower specificity (72% vs. 78–100%) in comparison to 8 single traditional ECG-LVH criteria. Additionally, the positive predictive value (77% vs. 72%), positive likelihood ratio (2.0 vs. 1.5), and odds ratio (3.2 vs. 2.4) were higher for the Peguero-Lo Presti criterion versus the presence of any of these 8 traditional ECG-LVH criteria. Cohen’s Kappa, a measure of concordance between ECG and echocardiography with regard to LVH, was 0.24 for the Peguero-Lo Presti criterion, −0.01–0.13 for single traditional criteria, and 0.20 for any traditional criterion. However, by the receiver operating characteristics (ROC) curve analysis, the overall ability to discriminate between patients with and without LVH was insignificantly lower for the Peguero-Lo Presti versus Cornell voltage as a continuous variable (area under the ROC curve: 0.65 (95% CI, 0.48–0.81) vs. 0.71 (0.55–0.86), p = 0.5). In conclusion, our preliminary results suggest a slightly better, albeit still low, agreement of the novel Peguero-Lo Presti ECG criterion compared to the traditional ECG-LVH criteria with echocardiographic LVH in severe aortic stenosis.

Estimating the area under a receiver operating characteristic curve using partially ordered sets

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ehsan Zamanzade ◽  
Xinlei Wang
Keyword(s):  
Roc Curve ◽  
Characteristic Curve ◽  
Partially Ordered Sets ◽  
Sampling Technique ◽  
Real Data ◽  
Cost Effective ◽  
Ordered Sets ◽  
Operating Characteristics ◽  
Partially Ordered ◽  
Receiver Operating

AbstractRanked set sampling (RSS), known as a cost-effective sampling technique, requires that the ranker gives a complete ranking of the units in each set. Frey (2012) proposed a modification of RSS based on partially ordered sets, referred to as RSS-t in this paper, to allow the ranker to declare ties as much as he/she wishes. We consider the problem of estimating the area under a receiver operating characteristics (ROC) curve using RSS-t samples. The area under the ROC curve (AUC) is commonly used as a measure for the effectiveness of diagnostic markers. We develop six nonparametric estimators of the AUC with/without utilizing tie information based on different approaches. We then compare the estimators using a Monte Carlo simulation and an empirical study with real data from the National Health and Nutrition Examination Survey. The results show that utilizing tie information increases the efficiency of estimating the AUC. Suggestions about when to choose which estimator are also made available to practitioners.

scholarly journals Right choice of a method for determination of cut-off values: A statistical tool for a diagnostic test

2014 ◽  
Vol 5 (3) ◽  
pp. 30-34 ◽  
Author(s):  
Balkishan Sharma ◽  
Ravikant Jain
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Traditional Method ◽  
Statistical Technique ◽  
Curve Analysis ◽  
Operating Characteristics ◽  
Roc Curve Analysis

Objective: The clinical diagnostic tests are generally used to identify the presence of a disease. The cutoff value of a diagnostic test should be chosen to maximize the advantage that accrues from testing a population of human and others. When a diagnostic test is to be used in a clinical condition, there may be an opportunity to improve the test by changing the cutoff value. To enhance the accuracy of diagnosis is to develop new tests by using a proper statistical technique with optimum sensitivity and specificity. Method: Mean±2SD method, Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been discussed with their respective applications. Results: The study highlighted some important methods to determine the cutoff points for a diagnostic test. The traditional method is to identify the cut-off values is Mean±2SD method. Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been proved to be beneficial statistical tools for determination of cut-off points.Conclusion: There may be an opportunity to improve the test by changing the cut-off value with the help of a correctly identified statistical technique in a clinical condition when a diagnostic test is to be used. The traditional method is to identify the cut-off values is Mean ± 2SD method. It was evidenced in certain conditions that logistic regression is found to be a good predictor and the validity of the same can be confirmed by identifying the area under the ROC curve. Abbreviations: ROC-Receiver operating characteristics and DA-Discriminant Analysis. Asian Journal of Medical Science, Volume-5(3) 2014: 30-34 http://dx.doi.org/10.3126/ajms.v5i3.9296      

scholarly journals Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Endre Grøvik ◽  
Darvin Yi ◽  
Michael Iv ◽  
Elizabeth Tong ◽  
Line Brennhaug Nilsen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Metastases ◽  
Multicenter Study ◽  
Roc Curve ◽  
Inversion Recovery ◽  
Fast Spin Echo ◽  
Operating Characteristics ◽  
Test Set ◽  
Mri Sequences ◽  
Input Level

AbstractThe purpose of this study was to assess the clinical value of a deep learning (DL) model for automatic detection and segmentation of brain metastases, in which a neural network is trained on four distinct MRI sequences using an input-level dropout layer, thus simulating the scenario of missing MRI sequences by training on the full set and all possible subsets of the input data. This retrospective, multicenter study, evaluated 165 patients with brain metastases. The proposed input-level dropout (ILD) model was trained on multisequence MRI from 100 patients and validated/tested on 10/55 patients, in which the test set was missing one of the four MRI sequences used for training. The segmentation results were compared with the performance of a state-of-the-art DeepLab V3 model. The MR sequences in the training set included pre-gadolinium and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D fluid attenuated inversion recovery (FLAIR), whereas the test set did not include the IR prepped image-series. The ground truth segmentations were established by experienced neuroradiologists. The results were evaluated using precision, recall, Intersection over union (IoU)-score and Dice score, and receiver operating characteristics (ROC) curve statistics, while the Wilcoxon rank sum test was used to compare the performance of the two neural networks. The area under the ROC curve (AUC), averaged across all test cases, was 0.989 ± 0.029 for the ILD-model and 0.989 ± 0.023 for the DeepLab V3 model (p = 0.62). The ILD-model showed a significantly higher Dice score (0.795 ± 0.104 vs. 0.774 ± 0.104, p = 0.017), and IoU-score (0.561 ± 0.225 vs. 0.492 ± 0.186, p < 0.001) compared to the DeepLab V3 model, and a significantly lower average false positive rate of 3.6/patient vs. 7.0/patient (p < 0.001) using a 10 mm3 lesion-size limit. The ILD-model, trained on all possible combinations of four MRI sequences, may facilitate accurate detection and segmentation of brain metastases on a multicenter basis, even when the test cohort is missing input MRI sequences.

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