基于机器学习阿尔茨海默病多模态磁共振的初步研究OA

Objective:Based on interpretable machine learning methods,to explore the application value of multimodal MRI radiomics in the diagnosis of Alzheimer's disease(AD)and provide imaging tools for accurate clinical diagnosis of AD.Methods:A retrospective study was conducted on 110 subjects,including 48 in the AD group and 62 in the control group(HC),all of whom completed 3D-T1WI,DWI,and T2WI MRI sequence scans.Randomly stratified sampling was conducted at a ratio of 7:3 to divide the data into training and testing sets,and 8 AD related core brain regions(hippocampus,entorhinal cortex,etc.)were segmented.Extract 107 radiomics features,screen the core features,and construct 8 diagnostic models(2 algorithms x 3 sequences,2 algorithms x 2 sequences combined)based on logistic regression(LR)and random forest(RF)algorithms.Evaluate the model performance using area under the curve(AUC),and analyze the model interpretability using SHAP analysis.Results:16 core radiomics features were ultimately selected.The joint sequence model has the best diagnostic performance,with AUC values of 0.989(95%CI:0.960～1.00)and 0.970(95%CI:0.920～1.00)for the LR and RF algorithm test sets,respectively,which are significantly higher than those of the single sequence model;The accuracy,sensitivity,and specificity of the LR joint model test set were 0.882,0.800,and 0.947,respectively.SHAP analysis shows that the 3D-T1WI sequence with short run length and high grayscale emphasized features,and the DWI sequence with grayscale co-occurrence matrix information measurement are the core indicators for AD diagnosis.Conclusions:The multimodal MRI radiomics model can efficiently achieve AD diagnosis,and the LR combined model has the best comprehensive performance.SHAP analysis can clearly analyze the decision-making basis of the model,providing strong support for the clinical translation of the model and accurate diagnosis of AD.