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 Selected Robust Logistic Regression Specification for Classification of Multi‑dimensional Functional Data in Presence of Outlier

2018 ◽  
Vol 2 (334) ◽  
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Smaga
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Data ◽  
Logistic Regression Model ◽  
Binary Classification ◽  
Classification Problem ◽  
Classification Rule ◽  
Unknown Parameters ◽  
Explanatory Variables

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

Classification of array CGH data using smoothed logistic regression model

2009 ◽  
Vol 28 (30) ◽  
pp. 3798-3810 ◽  
Author(s):  
Jian Huang ◽  
Agus Salim ◽  
Kaibin Lei ◽  
Kathleen O'Sullivan ◽  
Yudi Pawitan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Array Cgh

Quality Classification Model of Material Products Based on Ordinal Logistic Regression

2012 ◽  
Vol 460 ◽  
pp. 393-397 ◽  
Author(s):  
Peng Fei Mu ◽  
Dong Ling Zhang ◽  
Xiao Mei Xu ◽  
Yang Liu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Quality Evaluation ◽  
Ordinal Logistic Regression ◽  
Processing Method ◽  
Classification Model ◽  
Classification Result ◽  
Quality Classification ◽  
Ordinal Logistic Regression Model

It presents a proposed method for the development of quality evaluation and classification for material products, and shows the application of the ordinal logistic regression model and its advantages. It involved several steps: applying the linguistic information processing method, building the ordinal logistic regression model, differentiating and analyzing the quality evaluation to reach the quality classification result

Moving Shadow Detection Method Using Logistic Regression

2014 ◽  
Vol 543-547 ◽  
pp. 2724-2727
Author(s):  
Liu Yang ◽  
Jiang Yan Dai ◽  
Miao Qi ◽  
Qing Ji Guan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Moving Objects ◽  
Detection Method ◽  
Experimental Results ◽  
Shadow Detection ◽  
Video Frames ◽  
Moving Shadow Detection

We present a novel moving shadow detection method using logistic regression in this paper. First, several types of features are extracted from pixels in foreground images. Second, the logistic regression model is constructed by random pixels selected from video frames. Finally, for a new frame in one video, we take advantage of the constructed regression model to implement the classification of moving shadows and objects. To verify the performance of the proposed method, we test it on several different surveillance scenes and compare it with some well-known methods. Extensive experimental results indicate that the proposed method not only can separate moving shadows from moving objects accurately, but also is superior to several existing methods.

scholarly journals LogSum + L2 penalized logistic regression model for biomarker selection and cancer classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiao-Ying Liu ◽  
Sheng-Bing Wu ◽  
Wen-Quan Zeng ◽  
Zhan-Jiang Yuan ◽  
Hong-Bo Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cancer Classification ◽  
Classification Problems ◽  
Proposed Model ◽  
Different Types ◽  
Penalized Logistic Regression ◽  
Biomarker Selection

AbstractBiomarker selection and cancer classification play an important role in knowledge discovery using genomic data. Successful identification of gene biomarkers and biological pathways can significantly improve the accuracy of diagnosis and help machine learning models have better performance on classification of different types of cancer. In this paper, we proposed a LogSum + L2 penalized logistic regression model, and furthermore used a coordinate decent algorithm to solve it. The results of simulations and real experiments indicate that the proposed method is highly competitive among several state-of-the-art methods. Our proposed model achieves the excellent performance in group feature selection and classification problems.

scholarly journals Comparison of IOTA three-step strategy and logistic regression model LR2 for discriminating between benign and malignant adnexal masses

2020 ◽  
Author(s):  
Juan José Hidalgo ◽  
Antoni Llueca ◽  
Irene Zolfaroli ◽  
Nadia Veiga ◽  
Ester Ortiz ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Transvaginal Ultrasound ◽  
Ovarian Tumour ◽  
Diagnostic Strategy ◽  
Diagnostic Systems ◽  
Likelihood Ratios ◽  
Adnexal Masses

Aims: To compare the diagnostic performance of two ultrasound-based diagnostic systems for the classification of benign or malignant adnexal masses, the three-step strategy and the predictive logistic regression model LR2, both proposed by the International Ovarian Tumour Analysis (IOTA) Group. Material and methods: Prospective observational study at a single centre that included patients diagnosed with a persistent adnexal mass by transvaginal ultrasound over a period of two years. They were evaluated by a non-expert sonographer by applying the three-step diagnostic strategy and the LR2 predictive model to classify the masses as benign or malignant. Patients were treated surgically or followed up for at least one year, taking as the standard reference for benignity or malignancy the histological diagnosis of the lesion or ultrasound changes suggestive of malignancy during the follow-up period. Sensitivity, specificity, positive and negative likelihood ratios and overall accuracy of both systems was calculated and compared. Results: One hundred patients were included, with a mean age of 50.6 years (range 18-87). Surgery was performed on 62 (62%) patients and 38 (38%) were managed expectantly. Eighty-three (83%) lesions were benign and 17 (17%) were malignant. The IOTA three-step strategy presented sensitivity of 94.1% (95%CI, 86.7-98.3%) and specificity 97.6% (95%CI, 94.8-99%). The LR2 logistic regression model showed sensitivity 94.1% (95%CI, 73-98.9%) and specificity 81.9% (95%CI 72.3-88.7%). Comparison of the two systems showed a statistically significant dif-ference in specificity in favour of the three-step strategy. Conclusions: The IOTA three-step strategy, in addition to being sim-ple to use in clinical practice, has a high diagnostic accuracy for the classification of benignity and malignancy of the adnexal masses, overtaking that of other predictive models such as the LR2 logistic regression model.

scholarly journals Texture analysis of dark intraplacental bands on T2WI in the evaluation of placenta accreta spectrum disorders: a feasibility study

2021 ◽  
Author(s):  
Lishan Dong ◽  
Hailin Shen ◽  
Sheng Wang ◽  
Zhiyi Lei ◽  
Jiangong Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Texture Analysis ◽  
Logistic Regression Model ◽  
Placenta Accreta ◽  
Objective Assessment ◽  
Registration Number ◽  
Placenta Accreta Spectrum ◽  
Spectrum Disorders

Abstract Background: To evaluate whether texture analysis of dark intraplacental bands on T2WI can provide a novel methodological viewpoint valuable in assessing the classification of placenta accreta spectrum disorders (PAS disorders).Methods: 174 participants with suspected PAS disorders were consecutively included in the study. Texture analysis was implemented on dark intraplacental bands on T2WI by MaZda software. The two steps of parameter selection and reduction led to a decrease of the parameter space dimensionality. The logistics regression models were constructed with texture parameters to evaluate the classification of PAS disorders.Results: Both run length nonuniformity (RLN) and grey level nonuniformity (Gle) of four directions showed significant differences between participants with placenta accreta, increta and percreta (P﹤0.05). The AUC and cut-off for logistic regression model of accreta vs increta were 0.75 (95% CI: 0.54, 0.90) and 6.72, respectively; corresponding values for logistic regression model of increta vs percreta were 0.81 (95% CI: 0.61, 0.93) and 10.92. The sensitivity and specificity for cut-off of 6.72 were 88.46% and 84.62%, respectively; corresponding values for cut-off of 10.92 were 92.59% and 85.71%.Conclusion: Texture analysis offered promise for more quantitative and objective assessment of PAS disorders than other image modalities. It may be a useful add-on to MRI in evaluating the classification of PAS disorders. Trial registration: Registration number: ChiCTR2000038604 and name of registry: Evaluation of diagnostic accuracy of MRI multi-parameter imaging combined with texture analysis for placenta accreta spectrum disorders (PAD).

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.

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