超声影像组学对移植肾实质性病变鉴别诊断的价值  

The value of ultrasonography in the differential diagnosis of parenchymal lesions of transplanted kidney

作　　者：王天驰[1] 王众[1] 牛宁宁[1] 唐缨[1] WANG Tianchi;WANG Zhong;NIU Ningning;TANG Ying(Department of Ultrasonograph,Tianjin First Central Hospital,Tianjin 300192,China)

机构地区：[1]天津市第一中心医院超声科,300192

出　　处：《天津医药》2023年第6期653-657,共5页Tianjin Medical Journal

基　　金：国家自然科学基金资助项目(82172031);天津市科技计划项目(21JCYBJC01800)。

摘　　要：目的探讨超声影像组学对移植肾实质性病变组织学的诊断价值。方法选取186例因血肌酐异常而行肾穿刺活检的同种异体肾移植患者,根据活检结果分为急性排斥反应组(AR组)135例和肾小管坏死组(ATN组)51例。收集移植肾穿刺活检结果和超声资料,由2位医师根据常规超声参数进行诊断;应用影像组学进行超声图像特征提取,对获得的全部组学特征数据采用独立样本t检验进行初次筛选,再使用最小绝对值收敛和选择算子(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、K近邻法、逻辑回归、支持向量机分类器建立预测模型。所有患者按照7∶3的比例分配到训练队列和验证队列,采用5折交叉验证策略分析各组学模型在验证队列的准确度、敏感度、特异度、ROC曲线下面积(AUC)。结果医师组以常规超声参数为特征对AR和ATN鉴别诊断的敏感度为56.2%,特异度为60.7%,准确度为57.5%。应用影像组学方法每幅图像提取137个组学特征,经过筛选后最终保留6个有意义的特征,分别为2D形状-平坦度、一阶-最小值、直方图-最小值、直方图-体素计数、梯度-标准差、灰度共生矩阵-集群阴影。随机森林、支持向量机、逻辑回归和K近邻法4种模型的AUC分别为0.931(95%CI:0.779~0.997)、0.762(95%CI:0.604~0.897)、0.721(95%CI:0.582~0.808)和0.713(95%CI:0.508~0.796),其中随机森林模型敏感度为97.60%,特异度为80.00%,准确度为85.80%,综合表现最优。结论超声影像组学可以提取更多的超声图像特征,各组学模型对移植肾实质性病变组织学分型均具有较好的鉴别诊断价值,优于常规超声方法。Objective To investigate the diagnostic value of ultrasound radiomics for the histology of substantial lesions in transplanted kidney.Methods A total of 186 allograft patients who underwent renal puncture biopsy due to abnormal creatinine were selected and divided into the acute rejection(AR)group(135 cases)and the tubular necrosis(ATN)group(51 cases)according to the biopsy results.The biopsy results and ultrasonic data of the transplanted kidney were collected.The diagnosis was made by two physicians according to conventional ultrasound parameters.Radiomics was applied for ultrasonic image feature extraction.Independent sample t test was used for the initial selection of all the acquired omics feature data,and then the least absolute shrinkage and selection operator(LASSO)algorithm were used to select the best effective features from the selected features.Random forest,K-nearest neighbor method,Logistic regression and support vector machine classifier were used to establish the prediction model.All patients were assigned to the training cohort and the validation cohort according to the ratio of 7∶3,and a 5-fold cross-validation strategy was used to analyze the accuracy,sensitivity,specificity and ROC area under curve(AUC)of each histological model validation cohort.Results In the physician group,the sensitivity was 56.2%,specificity was 60.7%,and accuracy was 57.5%of the differential diagnosis of AR and ATN based on conventional ultrasound parameters.The image omics method was applied to extract 137 histological features from each image,and 6 meaningful features were retained after screening,including Shape2D-Flatness,FirstOrder-Min,Histo-Min,Histo-VoxelCount,Grad-Std and GLCM-CS.The AUCs of random forest,support vector machine,Logistic regression and K-nearest neighbor method were 0.931(95%CI:0.779-0.997),0.762(95%CI:0.604-0.897),0.721(95%CI:0.582-0.808)and 0.713(95%CI:0.508-0.796)respectively,in which the sensitivity,specificity and accuracy of the random forest model were 97.60%,80.00%and 85.80%,showing the

关 键 词：超声检查 移植物排斥 肾移植 肾小管坏死 急性 人工智能 组织学 影像组学 

分 类 号：R445.1[医药卫生—影像医学与核医学] R699.2[医药卫生—诊断学]