摘要
目的探讨基于动态增强MRI(DCE-MRI)和扩散加权成像(DWI)的影像组学模型对小乳腺癌(最大径≤20 mm)的诊断效能,并与放射科医师评估结果进行对比分析。方法回顾性分析2016年6月至2018年1月上海交通大学医学院附属仁济医院经手术病理证实的乳腺小病灶(最大径≤20 mm)205个,分为训练集(n=116)和测试集(n=89)。基于术前DCE-MRI和DWI序列,运用梯度提升决策树(GBDT)建立影像组学模型,预测测试集病灶的良恶性。测试集MRI图像由1名经验丰富的放射科医师评估,判断病灶良恶性。运用受试者操作特征(ROC)曲线分析评估GBDT模型和放射科医师的诊断效能。采用DeLong检验比较ROC曲线下面积,McNemar检验比较灵敏度、特异度和准确度。结果GBDT模型鉴别乳腺小病灶良恶性的ROC曲线下面积(0.950),与放射科医师联合DCE-MRI和DWI评估(0.935)相比差异无统计学意义(Z=0.499,P=0.618),并显著高于单独运用DCE-MRI(0.874)或DWI(0.832)评估(Z=2.024,P=0.043;Z=2.772,P=0.006)。GBDT模型最佳截断点灵敏度、特异度和准确度分别为90.0%,89.8%和89.9%,DCE-MRI联合DWI最佳截断点灵敏度、特异度和准确度分别为97.5%,79.6%和87.6%,2种方法相比差异均无统计学意义(χ^2=0.800,2.286和0.083,P均>0.05)。结论基于DCE-MRI和DWI建立的影像组学模型对小乳腺癌具有较高的诊断效能,与经验丰富的放射科医师评估结果一致。
Objective To evaluate the diagnostic performance of a radiomics model based on dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)in small breast cancer(≤20 mm in greatest dimension),and to compare the results with those of an experienced radiologist’s interpretation.Methods A total of 205 small breast lesions in 192 consecutive female patients from June 2016 to January 2018 at Renji Hospital,School of Medicine,Shanghai Jiaotong University,were retrospectively enrolled in the study.All lesions(≤20 mm in greatest dimension)were confirmed by surgical pathological results.The lesions were divided into a training set(116 lesions)and an independent test set(89 lesions).Based on preoperative breast DCE-MRI and DWI data,a radiomics model was built using gradient boosting decision tree(GBDT).The GBDT model was applied to the test set for differentiation between malignant and benign small breast lesions.Cases of the test set were also evaluated by an experienced radiologist for benign and malignant diseases differentiation.ROC curve was used to assess the diagnostic performance for the GBDT model and the radiologist evaluation,respectively.Differences in the area under the ROC curve(AUC)were analyzed by the DeLong test.Differences in sensitivity,specificity and accuracy were evaluated by the McNemar test.Kappa values were used to assess the agreement between different evaluation methods.Results The AUC of the GBDT model(0.950)showed no significant difference from that of the radiologist’s evaluation based on DCE-MRI combing DWI data(0.935)(Z=0.499,P=0.618).However,it showed the AUC of GBDT model was significantly higher than that of evaluation based on DCE-MRI(0.874)or DWI(0.832)alone(Z=2.024,P=0.043;Z=2.772,P=0.006).The sensitivity,specificity and accuracy of the best cutoff point of GBDT model were 90.0%,89.8%and 89.9%respectively.The sensitivity,specificity and accuracy of evaluation based on DCE-MRI combined with DWI were 97.5%,79.6%and 87.6%respectively.There was no significant differenc
作者
张庆
庄治国
耿小川
所世腾
华佳
许建荣
Zhang Qing;Zhuang Zhiguo;Geng Xiaochuan;Suo Shiteng;Hua Jia;Xu Jianrong(Department of Radiology,Renji Hospital,School of Medicine,Shanghai Jiaotong University,Shanghai 200127,China)
出处
《中华放射学杂志》
CAS
CSCD
北大核心
2020年第8期774-780,共7页
Chinese Journal of Radiology
基金
上海市科学技术委员会高新技术领域重点项目(18511102900,18511102901)
上海申康医院发展中心专科疾病临床“五新”转化项目(16CR3024A)。
关键词
乳腺肿瘤
磁共振成像
影像组学
Breast neoplasms
Magnetic resonance imaging
Radiomics
