- Model-Based Methods for Detection of Pulmonary Nodules

2016 ◽  
pp. 252-279
Keyword(s):  
Pulmonary Nodules ◽  
Model Based

scholarly journals A Prediction Model Based on Biomarkers and Clinical Characteristics for Detection of Lung Cancer in Pulmonary Nodules

2017 ◽  
Vol 10 (1) ◽  
pp. 40-45 ◽  
Author(s):  
Jie Ma ◽  
Maria A. Guarnera ◽  
Wenxian Zhou ◽  
HongBin Fang ◽  
Feng Jiang
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Clinical Characteristics ◽  
Pulmonary Nodules ◽  
Model Based

scholarly journals A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenqun Xing ◽  
Haibo Sun ◽  
Chi Yan ◽  
Chengzhi Zhao ◽  
Dongqing Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Prediction Model ◽  
Mayo Clinic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Pulmonary Nodules ◽  
Receiver Operator Characteristic Curve ◽  
Support Vector ◽  
Model Based

Abstract Background Lung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is a clinical challenge. This study aimed to develop a prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant pulmonary nodules from benign PNs. Methods We assessed three DNA methylation biomarkers (PTGER4, RASSF1A, and SHOX2) and clinically-relevant variables in a training cohort of 110 individuals with PNs. Four machine-learning-based prediction models were established and compared, including the K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithms. Variables of the best-performing algorithm (LR) were selected through stepwise use of Akaike’s information criterion (AIC). The constructed prediction model was compared with the methylation biomarkers and the Mayo Clinic model using the non-parametric approach of DeLong et al. with the area under a receiver operator characteristic curve (AUC) analysis. Results A prediction model was finally constructed based on three DNA methylation biomarkers and one radiological characteristic for identifying malignant from benign PNs. The developed prediction model achieved an AUC value of 0.951 in malignant PNs diagnosis, significantly higher than the three DNA methylation biomarkers (0.912, 95% CI:0.843–0.958, p = 0.013) or Mayo Clinic model (0.823, 95% CI:0.739–0.890, p = 0.001). Validation of the prediction model in the testing cohort of 100 subjects with PNs confirmed the diagnostic value. Conclusion We have shown that integrating DNA methylation biomarkers and radiological characteristics could more accurately identify lung cancer in subjects with CT-found PNs. The prediction model developed in our study may provide clinical utility in combination with LDCT to improve the over-all diagnosis of lung cancer.

scholarly journals A Prediction Model Based on DNA Methylation Biomarkers and Radiological Characteristics for Identifying Malignant From Benign Pulmonary Nodules

2020 ◽  
Author(s):  
Wenqun Xing ◽  
Haibo Sun ◽  
Chi Yan ◽  
Chengzhi Zhao ◽  
Dongqing Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Prediction Model ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Pulmonary Nodules ◽  
Diagnostic Value ◽  
Receiver Operator Characteristic Curve ◽  
Model Based ◽  
Diagnosis Of Lung Cancer

Abstract BackgroundLung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is a clinical challenge. This study aimed to develop a prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant pulmonary nodules from benign PNs. MethodsWe assessed three DNA methylation biomarkers (PTGER4, RASSF1A, and SHOX2) in a training cohort of 110 individuals with PNs. Using univariate and multivariate logistic regression analysis, we developed a prediction model based on the three DNA methylation biomarkers and one radiological characteristic for identifying malignant from benign PNs. The performance of the prediction model with that of the methylation biomarkers and the Mayo Clinic model were compared using the non-parametric approach of DeLong et al. with the area under a receiver operator characteristic curve (AUC) analysis. ResultsThe developed prediction model achieved a sensitivity of 87.3% and a specificity of 95.7% with an AUC value of 0.951 in malignant PNs diagnosis, being significantly higher than the three DNA methylation biomarkers (84.1% sensitivity and 89.4% specificity, p=0.013) or clinical/radiological characteristics (76.2% sensitivity and 87.2% specificity, p=0.001) alone. Validation of the prediction model in the testing cohort of 100 subjects with PNs confirmed the diagnostic value.ConclusionWe have shown that integrating DNA methylation biomarkers and radiological characteristics could more accurately identify lung cancer in subjects with CT-found PNs. The prediction model developed in our study may provide clinical utility in combination with LDCT to improve the over-all diagnosis of lung cancer.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Model-Based Software Testing and Analysis with C#

2007 ◽  
Author(s):  
Jonathan Jacky ◽  
Margus Veanes ◽  
Colin Campbell ◽  
Wolfram Schulte
Keyword(s):  
Software Testing ◽  
Model Based
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