PET/MRI hippocampal texture analysis for diagnosing Alzheimer disease and amnestic mild cognitive impairment
Objective To investigate the diagnostic value of PET/MRI hippocampal texture analysis for diagnosing Alzheimer disease(AD)and amnestic mild cognitive impairment(aMCI).Methods Data of 55 patients with AD(AD group),60 patients with aMCI(aMCI group)and 55 healthy controls(HC group)were retrospectively analyzed.The subjects were randomly divided into training and testing sets in a ratio of 7:3.Simultaneous PET/MRI was performed to obtain 3D T1WI and 18F-FDG PET.Texture features of ROI on both side hippocampus in training set were extracted.Logistic regression(LR),support vector machine(SVM)and random forest(RF)were used to construct 3D T1WI models,18F-FDG PET models and combined models,respectively.Receiver operating characteristic curves were drawn to evaluate the efficacy of the above models for diagnosing AD and aMCI.Results The wavelet features accounted for the most of the optimal hippocampal texture features for diagnosing AD and aMCI.Based on LR,SVM and RF algorithms,the area under the curve(AUC)of the combined models(0.996,0.993,0.991)were all the highest for diagnosing AD in testing set,followed by 18F-FDG PET models(0.941,0.941,0.967),and of single-modal model were the lowest(0.801,0.801,0.750).Based on LR and RF algorithms,AUC of the combined models(0.967,0.992)were highest for diagnosing aMCI in testing set,followed by 18F-FDG PET models(0.951,0.971),and the 3D T1WI models had the lowest AUC(0.833,0.824).Based on SVM algorithm,AUC(0.951)of combined model and of 18F-FDG PET model for diagnosing aMCI in testing set were the same,both higher than that of 3D T1WI model(0.833).Conclusion PET/MRI hippocampal texture analysis was helpful for diagnosing AD and aMCI.Multimodal combined diagnosis was superior to single-modal,with good robustness across different machine learning models.
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