Comparing and combining MRE, T1ρ, SWI, IVIM, and DCE‐MRI for the staging of liver fibrosis in rabbits: Assessment of a predictive model based on multiparametric MRI

2021 ◽  
Author(s):  
Liqiu Zou ◽  
Jinzhao Jiang ◽  
Hao Zhang ◽  
Wenxin Zhong ◽  
Min Xiao ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Predictive Model ◽  
Multiparametric Mri ◽  
Dce Mri ◽  
Model Based

Identification of liver fibrosis in HIV/HCV-coinfected patients using a simple predictive model based on routine laboratory data

2007 ◽  
Vol 0 (0) ◽  
pp. 070901052026008-???
Author(s):  
J. Berenguer ◽  
J. M. Bellón ◽  
P. Miralles ◽  
E. Álvarez ◽  
M. Sánchez-Conde ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Predictive Model ◽  
Laboratory Data ◽  
Routine Laboratory ◽  
Model Based

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

Energy-Efficient Operation of a Utility Plant via Predictive Model-Based Optimization Technology

2013 ◽  
Vol 53 (13) ◽  
pp. 5274-5283
Author(s):  
Bijan Sayyar-Rodsari ◽  
Alexander B. Smith ◽  
Apurva Samudra
Keyword(s):  
Predictive Model ◽  
Energy Efficient ◽  
Efficient Operation ◽  
Model Based ◽  
Utility Plant

scholarly journals Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 286
Author(s):  
Clément Acquitter ◽  
Lucie Piram ◽  
Umberto Sabatini ◽  
Julia Gilhodes ◽  
Elizabeth Moyal Cohen-Jonathan ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Predictive Power ◽  
Clinical Observation ◽  
Predictive Performance ◽  
Multiparametric Mri ◽  
Random Forest Classifier ◽  
Additional Benefit ◽  
Image Normalization ◽  
Model Based

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.

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