摘要
目的探讨基于动态对比增强磁共振成像(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)图像深度学习模型鉴别诊断乳腺良恶性肿瘤的价值。材料与方法回顾性分析2018年9月至2022年12月厦门医学院附属第二医院病理学确诊303例乳腺肿瘤患者资料,良性144例,恶性159例。按7∶3的比例分层随机抽样患者,分成训练集212例、测试集91例。构建六个深度学习模型:50层深度残差网络(50-layer deep residual network,ResNet-50)、Inception-V3、Googlenet,密集连接的卷积网络(densely connected convolutional networks,DenseNet)-121、视觉几何组(visual geometry group,VGG)-19和移动神经网络(mobile neural network,MobileNet)-V3,同时应用梯度加权类激活映射(gradient-weighted class activation mapping,Grad-CAM)对模型进行可视化。最后通过第一、二轮阅片比较了深度学习模型、初级和高级放射科医师的诊断结果。通过受试者工作特征(receive operating characteristic,ROC)曲线、准确度、敏感度、特异度、阴性预测值(negative predictive value,NPV)及阳性预测值(positive predictive value,PPV)对不同深度学习模型及两轮阅片的诊断效能进行分析,计算各深度学习模型曲线下面积(area under the curve,AUC),使用DeLong检验对各模型间ROC曲线进行比较,使用配对卡方检验比较两轮阅片的诊断效能。结果训练集ResNet-50、Inception-V3、Googlenet、DenseNet-121、VGG-19和MobileNet-V3六种深度学习模型AUC分别为0.874[95%置信区间(confidence interval,CI):0.828~0.920]、0.771(95%CI:0.707~0.834)、0.993(95%CI:0.986~0.999)、0.926(95%CI:0.888~0.958)、0.947(95%CI:0.918~0.975)及0.945(95%CI:0.918~0.973)。测试集ResNet-50、Inception-V3、Googlenet、DenseNet-121、VGG-19和MobileNet-V3六种深度学习模型AUC分别为0.841(95%CI:0.755~0.927)、0.746(95%CI:0.641~0.851)、0.822(95%CI:0.736~0.909)、0.752(95%CI:0.650~0.855)、0.827(95%CI:0.737~0.918)及0.779(95%CI:0.685~0.874)。ResNet-50模型Grad-CAM可视化图像显示乳腺恶性肿瘤呈病灶中央激活,良性肿瘤呈周边激活。第一轮阅片,ResNet-50深度学习模型的准确度、特异度及敏感度分别为80.2%、86.7%及73.9%,初级医师的准确度、特异度及敏感度为73.6%、73.3%及73.9%,高级医师的准确度、特异度及敏感度为80.2%、80.0%及80.4%。第二轮阅片,在ResNet-50模型辅助下,初级医师准确度、特异度及敏感度增加15.4%、17.8%、13.1%(P<0.05),高级医师准确度、特异度及敏感度增加12.1%、13.3%、10.9%(P=0.001、0.031、0.063)。结论ResNet-50模型鉴别诊断良恶性乳腺肿瘤性能最佳,可视化图像可能成为影像诊断依据。借助该模型放射科医师鉴别诊断乳腺肿瘤良、恶性准确性明显提高,为临床决策提供客观依据。
Objective:To explore the value of image deep learning model based on dynamic contrast-enhanced magnetic resonance imaging in differential diagnosis of benign and malignant breast tumors.Materials and Methods:A total of 303 breast tumor patients diagnosed pathologically in the Second Affiliated Hospital of Xiamen Medical College from September 2018 to December 2022 were retrospectively collected,including 144 benign and 159 malignant.Stratified random sampling patients were divided into 212 training set and 91 test set according to the ratio of 7:3.Six DCE-MRI Deep learning models were constructed:50-layer deep residual network(ResNet-50),Inception-V3,Googlenet,Densely connected convolutional networks(DenseNet)-121,visual geometry group(VGG)-19 and mobile neural network(MobileNet)-V3 were used to visualize the model simultaneously with gradient-weighted class activation mapping.Finally,the diagnostic results of the deep learning model,junior and senior radiologists were compared by the first and second rounds of reading.According to the receiver operating characteristic(ROC)curve,accuracy,sensitivity,specificity,negative predictive value and positive predictive value analyze the diagnostic efficiency of different deep learning models and two rounds of reading,calculate the area under the curve of each deep learning model,compare the ROC curves among the models with DeLong test,and compare the diagnostic efficiency of two rounds of reading with paired chi-square test.Results:The AUC of the six deep learning models ResNet-50,Inception-V3,Googlenet,DenseNet-121,VGG-19 and MobileNet-V3 was 0.874[95%confidence interval(CI):0.828-0.920],0.771(95%CI:0.707-0.834),0.993(95%CI:0.986-0.999),0.926(95%CI:0.888-0.958),0.947(95%CI:0.918-0.975)and 0.945(95%CI:0.918-0.973).The test sets of ResNet-50,Inception-V3,Googlenet,DenseNet-121,VGG-19 and MobileNet-V3 had an AUC of 0.841(95%CI:0.755-0.927),0.746(95%CI:0.641-0.851),0.822(95%CI:0.736-0.909),0.752(95%CI:0.650-0.855),0.827(95%CI:0.737-0.918)and 0.779(95%CI:0.685-0.874).ResNet-50 model Grad-CAM visualization images showed that malignant breast tumors were activated in the center and benign tumors were activated in the periphery.In the first round of reading,the accuracy,specificity and sensitivity of ResNet-50 deep learning model were 80.2%,86.7%and 73.9%,junior doctors were 73.6%,73.3%and 73.9%,and senior doctors were 80.2%,80.0%and 80.4%,respectively.In the second round of reading,with the assistance of ResNet-50 model,the accuracy,specificity and sensitivity of junior doctors increased by 15.4%,17.8%and 13.1%(P<0.05),while the accuracy,specificity and sensitivity of senior doctors increased by 12.1%,13.3%and 10.9%(P=0.001,0.031,0.063).Conclusions:ResNet-50 model has the best performance in differential diagnosis of benign and malignant breast tumors,and visual images may become the basis of imaging diagnosis.With the help of this model,radiologists significantly improve the accuracy of differential diagnosis of benign and malignant breast tumors,which provides an objective basis for clinical decision-making.
作者
罗文斌
郑晔
刘欣
王蕾
段少银
LUO Wenbin;ZHENG Ye;LIU Xin;WANG Lei;DUAN Shaoyin(Department of Radiology,the second Affiliated Hospital of Xiamen Medical College,Xiamen 361021,China;Department of Radiology,Zhongshan Hospital Xiamen University,Xiamen 361004,China)
出处
《磁共振成像》
CAS
CSCD
北大核心
2024年第10期22-29,共8页
Chinese Journal of Magnetic Resonance Imaging
基金
福建省自然科学基金面上项目(编号:2022J011387)
厦门市医疗卫生指导性项目(编号:3502Z20214ZD1198)。
关键词
乳腺肿瘤
辅助诊断
深度学习
动态增强磁共振成像
卷积神经网络
breast neoplasms
auxiliary diagnosis
deep learning
dynamic contrast-enhanced magnetic resonance imaging
convolutional neural network
