动态对比增强MRI影像组学特征鉴别乳腺良恶性病变的价值  

The value of dynamic contras-t enhanced MRI radiomics in differentiating benign and malignant breast lesions

作　　者：宋德领 张玉姣 朱月香[2] 曲迎午 崔书君[2] SONG Deling;ZHANG Yujiao;ZHU Yuexiang;QU Yingwu;CUI Shujun(Graduate Faculty,Hebei North University,Zhangjiakou,Hebei Province 075000,China;Department of Radiology,the First Affiliated Hospital of Hebei North University,Zhangjiakou,Hebei Province 075000,China)

机构地区：[1]河北北方学院研究生院,河北张家口075000 [2]河北北方学院附属第一医院影像科,河北张家口075000

出　　处：《实用放射学杂志》2021年第11期1810-1813,1846,共5页Journal of Practical Radiology

摘　　要：目的探讨动态对比增强磁共振成像(DCE-MRI)影像组学特征对乳腺良恶性病变的诊断价值。方法回顾性分析经病理证实的244例乳腺病变患者,按照7:3的比例将患者随机分为训练组(n=170)和验证组(n=74)。从DCE-MRI图像的第2期中提取影像组学特征。采用最小绝对收缩和选择算子(LASSO)回归方法筛选出训练组的最佳特征并构建影像组学标签,采用受试者工作特征(ROC)曲线评价影像组学标签的诊断效能。结果共筛选出8个特征用于建立影像组学标签,训练组和验证组影像组学标签的曲线下面积(AUC)分别为0.891和0.878,分别以0.632和0.699为最佳诊断阈值,其敏感性和特异性分别为89.00%和74.29%;86.05%和83.87%。其中浸润性导管癌、黏液癌、髓样癌、纤维腺瘤和小管腺瘤的分类准确度较高,准确度分别为91.82%、100%、100%、83.72%和100%。结论利用DCE-MRI提取的影像组学特征有助于乳腺良恶性病变的鉴别诊断,联合LASSO回归方法筛选出的影像组学特征建立的影像组学标签具有较高的诊断预测性。Objective To explore the value of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)radiomics in differentiating benign and malignant breast lesions.Methods 244 patients with breast lesions confirmed by pathology were analyzed retrospectively.They were randomly divided into the training cohort(n=170)and validation cohort(n=74)at a ratio of 7︰3.The radiomics features were extracted from the second phase of DCE-MRI images.The least absolute shrinkage and selection operator(LASSO)regression method was used to screen optimal features and construct a radiomics signature in the training cohort.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic capability of the radiomics signatures.Results 8 features were selected to construct the radiomics signature.In the training and validation cohorts,the area under the curve(AUC)of radiomics signature differentiating benign and malignant lesions were 0.891 and 0.878,respectively,with the optimal thresholds of 0.632 and 0.699,sensitivity and specificity of 89.00%and 74.29%,86.05%and 83.87%respectively.The classification accuracies of invasive ductal carcinoma,mucinous carcinoma,medullary carcinoma,fibroadenoma and tubular adenoma were high,with the accuracy of 91.82%,100%,100%,83.72%,and 100%.Conclusion The radiomic features extracted from DCE-MRI are helpful in differential diagnosis of benign and malignant breast lesions.The radiomics signature established by combining the radiomics features screened by the LASSO algorithm has high diagnostic predictability.

关 键 词：乳腺 良恶性病变 磁共振成像 影像组学 

分 类 号：R655.8[医药卫生—外科学] R757.9[医药卫生—临床医学]