Vivian Y J Lee, Lauren Houston, Adam Perkovic, Jennifer Y Barraclough, Arianne Sweeting, Jie Yu, Robert A Fletcher, Clare Arnott
The Effect of Weight Loss Through Lifestyle Interventions in Patients With Heart Failure With Preserved Ejection Fraction-A Systematic Review and Meta-Analysis of Randomised Controlled Trials
Heart, lung & circulation
accurately predicting distant metastasis head neck cancer potential improve patient survival allowing early treatment intensification systemic therapy highrisk patients extracting large amounts quantitative features mining radiomics achieved success predicting treatment outcomes various diseases however several challenges associated conventional radiomic approaches including 1 optimally combine information extracted multiple modalities 2 construct models emphasizing different objectives different clinical applications 3 utilize fuse output obtained multiple classifiers overcome challenges propose unified model termed multifaceted radiomics mradiomics mradiomics deep learning stacked sparse autoencoder first utilized fuse features extracted different modalities one representation feature set multiobjective optimization model introduced mradiomics probabilitybased objective functions designed maximize similarity probability output true label vector finally mradiomics employs multiple base classifiers get diverse paretooptimal model set fuses output probabilities paretooptimal models evidential reasoning rule fusion errf strategy testing stage obtain final output probability experimental results show mradiomics stacked autoencoder outperforms model without autoencoder mradiomics obtained accurate results better balance sensitivity specificity singleobjective singleclassifierbased models