Development of a nomogram for differentiation between pure mucinous breast carcinomas and fibroadenomas based on DCE-MRI features
曲宁 1罗娅红1
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作者信息
1. 中国医科大学肿瘤医院(辽宁省肿瘤医院)医学影像科,辽宁 沈阳 110042
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摘要
目的:基于动态对比增强磁共振成像(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)表现构建乳腺单纯型黏液癌(pure mucinous breast carcinoma,PMBC)与T2加权成像(T2-weighted imaging,T2WI)明显高信号乳腺纤维腺瘤鉴别诊断的列线图模型,旨在提高对两种病变鉴别诊断的准确度.方法:回顾并分析64个PMBC和137个T2WI明显高信号纤维腺瘤病变的DCE-MRI表现.记录放射科医师的原始乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)诊断结果.将单因素分析差异有统计学意义的DCE-MRI特征纳入多因素logistic回归分析,建立影像学特征模型,绘制列线图.采用受试者工作特征(receiver operating characteristic,ROC)曲线的曲线下面积(area under curve,AUC)、灵敏度、特异度、准确度、阳性预测值(positive predictive value,PPV)和阴性预测值(negative predictive value,NPV)评价影像学特征模型的分类性能.绘制校正曲线,评价模型对病变分类的预测结果与实际结果的一致性.采用临床决策分析曲线(decision curve analysis,DCA)评估模型的临床应用价值.结果:放射科医师原始诊断的灵敏度、特异度、准确度、PPV和NPV分别为76.56%、73.00%、74.13%、56.98%和86.96%.多因素logistic分析显示,患者的年龄、病变的边缘、晚期内部强化特征、内部有无强化分隔和分叶特征是鉴别PMBC和纤维腺瘤的独立预测因子.模型的AUC、灵敏度、特异度、准确度、PPV和NPV分别为96.24%、87.50%、94.89%、92.54%、88.89%和94.20%.校正曲线显示模型的预测结果与实际结果高度一致.DCA显示,影像学特征模型鉴别两种病变的临床净获益明显高于将其全部视为PMBC或纤维腺瘤.结论:基于DCE-MRI表现构建的PMBC与纤维腺瘤鉴别诊断的列线图模型明显优于影像医师原始诊断,可提高对两种病变鉴别诊断的准确度.
Abstract
Objective:To develop a nomogram to differentiate pure mucinous breast carcinoma(PMBC)from fibroadenoma(FA)with high signal intensity on T2-weighted imaging(T2WI)based on dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)features,in order to improve the accuracy of differential diagnosis between them.Methods:DCE-MRI features of 64 PMBC lesions and 137 FA lesions with T2WI were analyzed retrospectively.The Breast Imaging Reporting and Data System(BI-RADS)classification from the original report was recorded.DCE-MRI features with statistical difference in univariate analysis were included in multivariate logistic regression analysis to develop DCE-MRI nomogram.Area under curve(AUC),sensitivity,specificity,accuracy,positive predictive value(PPV)and negative predictive value(NPV)of receiver operating characteristic(ROC)curve were used to evaluate DCE-MRI nomogram.The calibration curves were drawn to show the consistency between the predictive value and the true value.Decision curve analysis(DCA)was conducted to determine the clinical usefulness of DCE-MRI nomogram.Results:Sensitivity,specificity,accuracy,PPV and NPV calculated according to the BI-RADS classification from the original report were 76.56%,73.00%,74.13%,56.98%and 86.96%,respectively.Multivariate analysis showed that age,margin,delayed enhancement pattern,enhancing internal septation and extent of lobulation were independent predictors for differentiating PMBC from FA.AUC,sensitivity,specificity,accuracy,PPV and NPV of DCE-MRI nomogram were 96.24%,87.50%,94.89%,92.54%,88.89%and 94.20%,respectively.According to the calibration curve,the predicted and true values showed good consistency.Based on decision curve analysis,the net benefit of using DCE-MRI nomogram to differentiate PMBC from FA was greater than treat-all or treat-none scheme.Conclusion:The nomogram based on DCE-MRI features for differentiation between PMBC and FA was superior to the BI-RADS classification from the original report and improved the accuracy of differential diagnosis of PMBC and FA.
关键词
乳腺癌/乳腺单纯型黏液癌/纤维腺瘤/磁共振成像/列线图
Key words
Breast cancer/Pure mucinous breast carcinoma/Fibroadenoma/Magnetic resonance imaging/Nomogram
维普
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