摘要
目的探索基于T2WI的纹理分析和机器学习在区分肾乏脂血管平滑肌脂肪瘤(angiomyolipoma without visible fat,AMLwvf)和肾癌中的效能。材料与方法回顾分析80例肾脏肿瘤,包括肾AMLwvf、肾透明细胞癌、乳头状肾细胞癌和肾嫌色细胞癌各20例。软件勾画得到感兴趣容积,提取特征。克鲁斯卡尔-沃利斯检验提示肾癌亚型之间所有特征差异无统计学意义,故将肾癌亚型合并为肾癌组进行后续分析。单因素分析:通过非参数检验和ROC曲线寻找最佳特征,分析诊断效能。多特征建模:通过SPSS Modeler软件进行特征选择,构建并评价多个决策树C5.0模型。结果最佳特征为最小灰度,AUC为0.888,鉴别准确性为86.25%。最佳模型的AUC为0.950,诊断肾AMLwvf的敏感度为90.00%,特异度为100%,阳性预测值为100%,阴性预测值为96.77%,准确度为97.5%,交叉验证准确度为95.0%。结论基于T2WI的纹理分析和决策树C5.0模型可有效鉴别AMLwvf和肾癌。
Objective:To distinguish between renal angiomyolipoma without visible fat(AMLwvf)and renal cell carcinoma(RCC)using T2WI texture analysis and machine learning.Materials and Methods:80 cases of renal tumors were analyzed retrospectively,including AMLwvf(n=20),clear cell renal cell carcinoma(n=20),papillary renal cell carcinoma(n=20)and chromophobe renal cell carcinoma(n=20).Lesions were delineated on software by two radiologists to extract the corresponding volumes of interest(VOI)and then 93 features were generated.The Kruskal Wallis test showed that there was no significant difference between renal carcinoma subtypes,so renal carcinoma subtypes were combined into one group(renal carcinoma,n=60).Univariable analysis was carried out through Mann-Whitney U test and Holm-Bonferroni method to find the best features and analyze the diagnostic performance.Modeling with multiple features:after the primary selection of features by Pearson correlation coefficient,the C5.0 node of IBM SPSS modeler software calculated the relative importance ranking of features.Top 2,3,4 and 5 most important features were used to form 4 feature subsets.Decision tree C5.0 model was built with or without boosting.The differentiation and generalization ability of each model was evaluated to find the best one as the final model.Results:Univariable analysis:after Holm-Bonferroni correction,four different features were screened:minimum,10 percentile,difference variance and contrast.The area under the curve was 0.888,0.837,0.789 and 0.777,respectively.The range of positive predictive value was 50.00%—69.57%.Modeling with multiple features:8 decision tree C5.0 models were constructed.The area under the curve of final model was 0.950.The sensitivity,specificity,positive predictive value,negative predictive value and accuracy of final model were 90.00%,100%,100%,96.77%and 97.5%,respectively.The accuracy based on cross validation is 95.0%.Conclusions:Univariable analysis based on T2WI has limited clinical application value because of its low positiv
作者
刘震昊
白旭
叶慧义
郭爱桃
林明权
左盼莉
王海屹
LIU Zhenhao;BAI Xu;YE Huiyi;GUO Aitao;LIN Mingquan;ZUO Panli;WANG Haiyi(Department of Radiology,the first medical center of Chinese PLA General Hospital,Beijing 100853,China;Department of Radiology,Affiliated Hospital of Changzhi Institute of Traditional Chinese Medicine,Changzhi 046000,China;Department of Pathology,the first medical center of Chinese PLA General Hospital,Beijing 100853,China;Department of Electronic Engineering,City University of Hong Kong,Hong Kong 999077,China;Innovation and Collaboration Center,Huiying Medical Technology(Beijing)Co.,Ltd,Beijing 100192,China)
出处
《磁共振成像》
CAS
CSCD
北大核心
2021年第2期38-42,共5页
Chinese Journal of Magnetic Resonance Imaging
基金
国家自然科学基金(编号:81471641)。
关键词
肾肿瘤
磁共振成像
纹理分析
机器学习
乏脂血管平滑肌脂肪瘤
kidney neoplasms
magnetic resonance imaging
texture analysis
machine learning
angiomyolipoma without visible fat
