Brain age is an effective biomarker for diagnosing Alzheimer’s disease(AD).Aimed at the issue that the existing brain age detection methods are inconsistent with the biological hypothesis that AD is the accelerated a...Brain age is an effective biomarker for diagnosing Alzheimer’s disease(AD).Aimed at the issue that the existing brain age detection methods are inconsistent with the biological hypothesis that AD is the accelerated aging of the brain,a mutual information—support vector regression(MI-SVR)brain age prediction model is proposed.First,the age deviation is introduced according to the biological hypothesis of AD.Second,fitness function is designed based on mutual information criterion.Third,support vector regression and fitness function are used to obtain the predicted brain age and fitness value of the subjects,respectively.The optimal age deviation is obtained by maximizing the fitness value.Finally,the proposed method is compared with some existing brain age detection methods.Experimental results show that the brain age obtained by the proposed method has better separability,can better reflect the accelerated aging of AD,and is more helpful for improving the diagnostic accuracy of AD.展开更多
The high dimensions of hyperspectral imagery have caused burden for further processing. A new Fast Independent Component Analysis (FastICA) approach to dimensionality reduction for hyperspectral imagery is presented. ...The high dimensions of hyperspectral imagery have caused burden for further processing. A new Fast Independent Component Analysis (FastICA) approach to dimensionality reduction for hyperspectral imagery is presented. The virtual dimensionality is introduced to determine the number of dimensions needed to be preserved. Since there is no prioritization among independent components generated by the FastICA,the mixing matrix of FastICA is initialized by endmembers,which were extracted by using unsupervised maximum distance method. Minimum Noise Fraction (MNF) is used for preprocessing of original data,which can reduce the computational complexity of FastICA significantly. Finally,FastICA is performed on the selected principal components acquired by MNF to generate the expected independent components in accordance with the order of endmembers. Experimental results demonstrate that the proposed method outperforms second-order statistics-based transforms such as principle components analysis.展开更多
Background and Aims:The study established and compared the efficacy of the clinicoradiological model,radiomics model and clinicoradiological-radiomics hybrid model in predicting the microvascular invasion(MVI)of hepat...Background and Aims:The study established and compared the efficacy of the clinicoradiological model,radiomics model and clinicoradiological-radiomics hybrid model in predicting the microvascular invasion(MVI)of hepatocellular carcinoma(HCC)using gadolinium ethoxybenzyl diethylene triaminepentaacetic acid(Gd-EOB-DTPA)enhanced MRI.Methods:This was a study that enrolled 602 HCC patients from two institutions.Least absolute shrinkage and selection operator(Lasso)method was used to screen for the most important clinicoradiological and radiomics features that predict MVI pre-operatively.Three machine learning algorithms were used to establish the clinicoradiological,radiomics,and clinicoradiological-radiomics hybrid models.Area under the curve(AUC)of receiver operating characteristic(ROC)curves and Delong’s test were used to compare and quantify the predictive performance of the models.Results:The AUCs of the clinicoradiological model in training and validation cohorts were 0.793 and 0.701,respectively.The radiomics signature of arterial phase(AP)images alone achieved satisfying predictive efficacy for MVI,with AUCs of 0.671 and 0.643 in training and validation cohort,respectively.The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images achieved AUCs of 0.824 and 0.801 in training and validation cohorts,0.812 and 0.805 in prospective validation and external validation cohorts,respectively.The hybrid model provided the best prediction results.The results of the Delong test revealed that there were statistically significant differences among the clinicoradiological-radiomics hybrid model,clinicoradiological model,and radiomics model(p<0.05).Conclusions:The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images based on Gd-EOB-DTPA-enhanced MRI can effectively predict MVI.展开更多
基金the Natural Science Foundation of Chongqing(No.cstb2022nscq-msx1575)the Science and Technology Research Program of Chongqing Municipal Education Commission(Nos.KJQN202201512,KJQN202001523 and KJZD-M202101501)+1 种基金the Chongqing University of Science and Technology Research Funding Projects(Nos.CKRC2022019 and CKRC2019042)the Open Foundation of the Chongqing Key Laboratory for Oil and Gas Production Safety and Risk Control(No.cqsrc202113)。
文摘Brain age is an effective biomarker for diagnosing Alzheimer’s disease(AD).Aimed at the issue that the existing brain age detection methods are inconsistent with the biological hypothesis that AD is the accelerated aging of the brain,a mutual information—support vector regression(MI-SVR)brain age prediction model is proposed.First,the age deviation is introduced according to the biological hypothesis of AD.Second,fitness function is designed based on mutual information criterion.Third,support vector regression and fitness function are used to obtain the predicted brain age and fitness value of the subjects,respectively.The optimal age deviation is obtained by maximizing the fitness value.Finally,the proposed method is compared with some existing brain age detection methods.Experimental results show that the brain age obtained by the proposed method has better separability,can better reflect the accelerated aging of AD,and is more helpful for improving the diagnostic accuracy of AD.
基金Supported by the National Natural Science Foundation of China (No. 60572135)
文摘The high dimensions of hyperspectral imagery have caused burden for further processing. A new Fast Independent Component Analysis (FastICA) approach to dimensionality reduction for hyperspectral imagery is presented. The virtual dimensionality is introduced to determine the number of dimensions needed to be preserved. Since there is no prioritization among independent components generated by the FastICA,the mixing matrix of FastICA is initialized by endmembers,which were extracted by using unsupervised maximum distance method. Minimum Noise Fraction (MNF) is used for preprocessing of original data,which can reduce the computational complexity of FastICA significantly. Finally,FastICA is performed on the selected principal components acquired by MNF to generate the expected independent components in accordance with the order of endmembers. Experimental results demonstrate that the proposed method outperforms second-order statistics-based transforms such as principle components analysis.
基金supported by the National Key Research and Development Program of China (Nos.2016YFC0107101 and cstc2016shmszx130019).
文摘Background and Aims:The study established and compared the efficacy of the clinicoradiological model,radiomics model and clinicoradiological-radiomics hybrid model in predicting the microvascular invasion(MVI)of hepatocellular carcinoma(HCC)using gadolinium ethoxybenzyl diethylene triaminepentaacetic acid(Gd-EOB-DTPA)enhanced MRI.Methods:This was a study that enrolled 602 HCC patients from two institutions.Least absolute shrinkage and selection operator(Lasso)method was used to screen for the most important clinicoradiological and radiomics features that predict MVI pre-operatively.Three machine learning algorithms were used to establish the clinicoradiological,radiomics,and clinicoradiological-radiomics hybrid models.Area under the curve(AUC)of receiver operating characteristic(ROC)curves and Delong’s test were used to compare and quantify the predictive performance of the models.Results:The AUCs of the clinicoradiological model in training and validation cohorts were 0.793 and 0.701,respectively.The radiomics signature of arterial phase(AP)images alone achieved satisfying predictive efficacy for MVI,with AUCs of 0.671 and 0.643 in training and validation cohort,respectively.The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images achieved AUCs of 0.824 and 0.801 in training and validation cohorts,0.812 and 0.805 in prospective validation and external validation cohorts,respectively.The hybrid model provided the best prediction results.The results of the Delong test revealed that there were statistically significant differences among the clinicoradiological-radiomics hybrid model,clinicoradiological model,and radiomics model(p<0.05).Conclusions:The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images based on Gd-EOB-DTPA-enhanced MRI can effectively predict MVI.
