超声影像组学在肾炎组织学分型中的应用价值

The application value of ultrasound radiomics in the histological classification of nephritis

目的探讨基于灰阶超声图像的超声影像组学技术对肾小球肾炎组织学分型的鉴别诊断价值。方法纳入204例患者的肾穿刺活检病理结果和超声资料,根据病理结果分为膜性肾病组133例和系膜增生性肾小球肾炎组71例。由2位医师对超声影像进行勾画并提取影像组学特征,对获得的全部组学特征数据通过最大相关和最小冗余算法(m RMR)初步筛选超声影像组学特征,然后利用经最小绝对收缩和选择算子(LASSO)算法从已筛选特征中选择最优有效特征,并利用随机森林(RF)、支持向量机(SVM)、逻辑回归(LR)、K近邻(KNN)法4种分类器建立预测模型。所有病例按照7︰3比例随机分为训练集和验证集,4种模型分别利用训练集训练后,在验证集中验证,通过比较受试者工作特征(ROC)曲线、Delong检验、GiViTI校准曲线,选择最佳预测模型。采用决策曲线分析评估模型的临床实用性。结果从每幅图像提取837个影像组学特征,经m RMR+LASSO算法共筛选出16个有意义的特征。RF、SVM、LR、KNN 4种预测模型中表现最好的是LR模型,其ROC曲线下面积为0.944,特异度为0.867,敏感度为0.878。GiViTI校准曲线提示模型具有较好的准确度(P>0.05),决策曲线显示预测模型具有较好的临床实用价值。结论超声影像组学对较为常见的肾小球肾炎组织学类型具有较好的鉴别能力,具有良好的应用前景。

Objective To explore the application value of ultrasound radiomics technology based on grayscale ultrasound images in the differential diagnosis of histological classification of glomerulonephritis.Methods A total of 204 patients with renal biopsy were selected from our hospital,and according to pathological results,they were divided into the membranous nephropathy group(n=133)and the mesangial proliferative glomerulonephritis group(n=71).The ultrasound images were sketched and the image omics features were extracted by two physicians.The pathological results and ultrasound data of renal biopsy were collected from the two groups,and the ultrasound radiomics features were preliminarily screened by the maximum correlation and minimum redundancy algorithm(mRMR)algorithm for all the obtained omics feature data.Then the optimal effective features were selected from the screened features by minimum absolute shrinkage and selection operator(LASSO)algorithm,and random forest(RF),support vector machine(SVM),logistic regression(LR),four kinds of classifiers of K-nearest neighbor(KNN)method were used to establish a prediction model.All cases were randomly divided into the training set and the validation set according to the ratio of 7∶3,and the four models were trained by the training set,and then validated in the validation set,and the best prediction model was selected by comparing the receiver operating characteristic(ROC)curve,Delong test and GiViTI calibration curve.Decision curve analysis(DCA)was used to evaluate the clinical utility of the model.Results The radiomics method was used to extract 837 radiomics features per image,and 16 meaningful features were finally screened out by using the mRMR+LASSO algorithm.Among the four prediction models of RF,SVM,LR and KNN,the best performing model was LR model,with the AUC of 0.944,the specificity of 0.867 and the sensitivity of 0.878.The GiViTI calibration curve showed that the model had good accuracy(P>0.05),and the decision curve showed that the prediction model had good clinical practical value.Conclusion Ultrasound radiomics has a good ability to distinguish the more common histological types of glomerulonephritis,and is a non-invasive method with good application prospects.