Abstract: Objective To explore the differential diagnostic efficacy of machine learning models for MRI mild cognitive impairment(MCI) and alzheimer's disease(AD). Methods 513 MCI patients who visited our hospital from April 2022 to April 2025 were selected as the research subjects,and whether the patients developed AD was used as the stratification factor.The stratified random sampling method was used to divide the patients into a training set(n=359 cases) and a testing set(n=154 cases) in a 7:3 ratio.Based on whether MCI has been converted to AD,the training set patients were divided into AD group(n=122 cases) and MCI group(n=237 cases).Compared and analyze the differences in general information and experimental indicators of patients,and used multiple logistic regression to analyze the influencing factors of AD occurrence in patients;Adopted the Least Absolute Shrinkage and Selection Operator(LASSO) regression method for screening omics features and constructed an imaging omics model;The machine of learning algorithms,logistic regression(LR),support vector machine(SVM),random forest(RF),and decision tree(DT) were used to construct clinical prediction models and radiomics models based on independent influencing factors and radiomics,respectively and evaluated the diagnostic performance of each model. Results There were significant differences among the two groups in years of education,age,hypertension,diabetes,cerebrovascular history,negative life events,less physical exercise and MMSE(P＜0.05);The results of multiple logistic regression model showed that age,hypertension,diabetes,cerebrovascular history,marital status and education level were independent risk factors for AD,and physical exercise was protective factor(P＜0.05); The optimal imaging features obtained through LASSO regression screening were:wavelet-HH glcm ClusterShade, log-sigma-4-0-mm-3D firstorder 10Percentile, log-sigma-5-0-mm-3D glrlm RunVariance,log-sigma-3-0-mm-3Dgldm DependenceNonUniformityNormalized, log-sigma-2-0-mm-3D glcm Imcl, wavelet-LH glcm Imcl,original shape Flatness, 1og-sigma-5-0-mm-3D glszm ZoneVariance; Among the clinical prediction models and radiomics models constructed by four machine learning algorithms,the RF model was the optimal model,with AUC values of 0.841 and 0.821 for predicting AD occurrence in the training and testing sets, respectively;The AUC of radiomics models were 0.864 and 0.832, respectively;The AUC of the combined model were 0.896 and 0.857,respectively.The AUC of the combined model was significantly higher than that of any single model(P＜0.05);The analysis of calibration curve and clinical decision curve showed that the joint model had the highest calibration degree and the highest clinical net benefit. Conclusion The RF model based on the combination of clinical and radiomics can locate and identify MCI and AD with high accuracy,which can significantly improve the accuracy of automatic imaging diagnosis.

Key words: Mild cognitive impairment, Alzheimer's disease, Machine learning models, Radiomics, Diagnosis

摘要： 目的 探讨机器学习模型对MRI轻度认知障碍(MCI)和阿尔茨海默病(AD)的鉴别诊断效能。方法 选取2022年4月至2025年4月我院就诊的513例MCI患者作为研究对象,以患者是否发展为AD作为分层因素,采用分层随机抽样法将患者按照7:3比例分为训练集(n=359例)和测试集(n=154例),并根据MCI是否发展为AD将训练期患者分为AD组(n=122例)、MCI组(n=237例),对比分析患者一般资料及实验指标差异,采用多元Logistic回归分析患者AD发生的影响因素;采用最小绝对收缩和选择算子法(LASSO)回归方法进行组学特征的筛选,构建影像组学模型,采用4种机器学习算法Logistic 回归(LR)、支持向量机(SVM)、随机森林(RF)、决策树(DT)分别基于独立影响因素、影像组学构建临床预测模型和影像组学模型,并评价各模型的诊断效能。结果 两组患者在受教育年限、年龄、负性生活事件、高血压、脑血管病史、糖尿病、体育锻炼少、MMSE方面比较差异明显(P＜0.05);多元Logistic回归模型结果表明年龄、高血压、糖尿病、脑血管病史、负性生活事件、受教育水平是患者发生AD的独立危险因素,体育锻炼是保护因素(P＜0.05);经LASSO回归筛选得到最优影像学特征分别为:wavelet-HH glcm ClusterShade、log-sigma-4-0-mm-3D firstorder 10Percentile、log-sigma-5-0-mm-3D glrlm RunVariance、log-sigma-3-0-mm-3Dgldm DependenceNonUniformityNormalized、log-sigma-2-0-mm-3D glcm Imcl、wavelet-LH glcm Imcl、original shape Flatness、1og-sigma-5-0-mm-3D glszm ZoneVariance;在四种机器学习算法所构建的临床预测模型及影像组学模型中,RF模型均为最优模型,在训练集和测试集中预测AD发生的临床预测模型AUC分别为0.841、0.821;影像组学模型AUC分别为0.864、0.832;联合模型AUC分别为0.896、0.857,联合模型AUC明显高于其任一单一模型(P＜0.05);校准曲线及临床决策曲线分析显示联合模型校准度最高,临床净获益最高。结论 基于临床及影像组学联合的RF模型能够较高地定位和识别MCI及AD,可明显提高影像自动诊断的准确性。

关键词: 轻度认知障碍, 阿尔茨海默病, 机器学习模型, 影像组学, 诊断