Aneurysm Volume-to-Ostium Area Ratio

Neurosurgery ◽  
2011 ◽  
Vol 68 (2) ◽  
pp. 310-318 ◽  
Author(s):  
Ryuta Yasuda ◽  
Charles M. Strother ◽  
Waro Taki ◽  
Kazuhiko Shinki ◽  
Kevin Royalty ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Digital Subtraction Angiography ◽  
Logistic Regression Model ◽  
Aneurysm Rupture ◽  
Digital Subtraction ◽  
3 Dimensional ◽  
Aneurysm Size ◽  
Risk Of Rupture ◽  
Study Interval

Abstract BACKGROUND: Slow or stagnant flow is a hemodynamic feature that has been linked to the risk of aneurysm rupture. OBJECTIVE: To assess the potential value of the ratio of the volume of an aneurysm to the area of its ostium (VOR) as an indicator of intra-aneurysmal slow flow and, thus, in turn, the risk of rupture. METHODS: Using a sample defined from internal databases, a retrospective analysis of aneurysm size, aspect ratio (AR), and VOR was performed on a series of 155 consecutive aneurysms having undergone 3-dimensional digital subtraction angiography as a part of their evaluation. Measurements were obtained from 3-dimensional digital subtraction angiography studies using commercial software. Aneurysm size, AR, and VOR were correlated with rupture status (ruptured or unruptured). A multiple logistic regression model that best correlated with rupture status was generated to evaluate which of these parameters was the most useful to discriminate rupture status. This model was validated using an independent database of 62 consecutive aneurysms acquired outside the retrospective study interval. RESULTS: VOR showed better discrimination for rupture status than did size and AR. The best logistic regression model, which included VOR rather than size or AR, determined rupture status correctly in 80.6% of subjects. The reproducibility calculating AR and VOR was excellent. CONCLUSION: Determination of VOR was easily done and reproducible using widely available commercial equipment. It may be a more robust parameter to discriminate rupture status than AR.

scholarly journals CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

2021 ◽  
Vol 12 ◽  
Author(s):  
Osamah Alwalid ◽  
Xi Long ◽  
Mingfei Xie ◽  
Jiehua Yang ◽  
Chunyuan Cen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Intracranial Aneurysm ◽  
Regression Model ◽  
Ct Angiography ◽  
Logistic Regression Model ◽  
Aneurysm Rupture ◽  
Classification Model ◽  
High Morbidity ◽  
Unruptured Aneurysms

Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture.Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms.Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89–0.95] and 0.86 [95% CI: 0.80–0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. −1.60 and 2.35 vs. −1.01 on training and test cohorts, respectively, p < 0.001).Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.

Predicting aneurysm rupture probabilities through the application of a computed tomography angiography–derived binary logistic regression model

2009 ◽  
Vol 110 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Charles J. Prestigiacomo ◽  
Wenzhuan He ◽  
Jeffrey Catrambone ◽  
Stephanie Chung ◽  
Lydia Kasper ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Prospective Cohort ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Aneurysm Rupture ◽  
Binary Logistic Regression Model ◽  
Unruptured Aneurysms ◽  
Neck Size ◽  
The Status

Object The goal of this study was to establish a biomathematical model to accurately predict the probability of aneurysm rupture. Biomathematical models incorporate various physical and dynamic phenomena that provide insight into why certain aneurysms grow or rupture. Prior studies have demonstrated that regression models may determine which parameters of an aneurysm contribute to rupture. In this study, the authors derived a modified binary logistic regression model and then validated it in a distinct cohort of patients to assess the model's stability. Methods Patients were examined with CT angiography. Three-dimensional reconstructions were generated and aneurysm height, width, and neck size were obtained in 2 orthogonal planes. Forward stepwise binary logistic regression was performed and then applied to a prospective cohort of 49 aneurysms in 37 patients (not included in the original derivation of the equation) to determine the log-odds of rupture for this aneurysm. Results A total of 279 aneurysms (156 ruptured and 123 unruptured) were observed in 217 patients. Four of 6 linear dimensions and the aspect ratio were significantly larger (each with p < 0.01) in ruptured aneurysms than unruptured aneurysms. Calculated volume and aneurysm location were correlated with rupture risk. Binary logistic regression applied to an independent prospective cohort demonstrated the model's stability, showing 83% sensitivity and 80% accuracy. Conclusions This binary logistic regression model of aneurysm rupture identified the status of an aneurysm with good accuracy. The use of this technique and its validation suggests that biomorphometric data and their relationships may be valuable in determining the status of an aneurysm.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p &lt; 0,0001), education (p &lt; 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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