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不同模型动态对比增强磁共振成像在乳腺浸润性导管癌病理分级中的应用价值 被引量:4

Application value of different model of dynamic contrast-enhanced magnetic resonance imaging in pathological grading of breast invasive ductal carcinoma
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摘要 目的 探讨不同模型动态对比增强磁共振成像(DCE-MRI)在乳腺浸润性导管癌病理分级中的应用价值.方法 选取山西省人民医院2015年6月至2017年10月收治的45例经临床病理证实的乳腺浸润性导管癌患者,将1级和2级浸润性导管癌归为低级别浸润性导管癌组,3级浸润性导管癌归为高级别浸润性导管癌组.获取45例患者的DCE-MRI图像,首先使用结合人群平均动脉输入函数(Population AIF)的Extended Toffs Linear模型得到容积转运常数(Ktrans);然后使用Reference Region模型,得到参照区域模型容积转运常数(RRKtrams),对比两种模型得到的定量参数的直方图分析结果在乳腺浸润性导管癌病理分级中的效能.结果 高级别乳腺浸润性导管癌组RRKtrans的均值,25%、50%、75%、90%分位数,峰度及偏度分别为(0.793±0.258)/min、(0.484±0.209)/min、(0.773±0.277)/min、(1.066±0.351)/min、(1.322±0.406)/min、2.647(1.426,3.679)、0.398(0.297,0.514);低级别乳腺浸润性导管癌组分别为(0.506±0.203)/min、(0.301±0.142)/min、(0.487±0.211)/min、(0.692±0.281)/min、(0.861±0.323)/min、1.725(0.779,2.316)、0.258(0.133,0.302),两组间比较差异均有统计学意义(均P< 0.05).高级别乳腺浸润性导管癌组K^trans的均值,50%、75%、90%分位数分别为(0.099±0.034)/min、(0.110±0.033)/min、(0.132±0.045)/min、(0.140±0.047)/min;低级别乳腺浸润性导管癌组分别为(0.067±0.030)/min、(0.082±0.067)/min、(0.096±0.059)/min、(0.113 ±0.074)/min,两组间比较差异均有统计学意义(均P<0.05).RRK^trans鉴别高、低级别乳腺浸润性导管癌的受试者工作特征曲线下面积均大于K^trans.结论 Reference Region模型以及Population AIF模型DCE-MRI得到的RRK^trans、K^trans对高、低级别乳腺浸润性导管癌的病理分级具有一定价值,但前者的效能整体优于后者. Objective To explore the application value of different dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) models in pathological grading of breast invasive ductal carcinoma.Methods Forty-five cases of breast invasive ductal carcinoma confirmed by clinical pathology from June 2016 to October 2017 in Shanxi Provincial People's Hospital were enrolled in this study.Grade 1 and 2 invasive ductal carcinomas were classified into the low-grade group,grade 3 invasive ductal carcinomas was classified as the high-grade group.The whole patients underwent DCE-MRI scans.Volume transport constant (Ktrans) was obtained by using the Extended Tofts Linear model with population arterial input function (AIF).Then,the Reference Region model was used to obtain the reference region model volume transport constant(RRKtrans).The performances of histogram analysis of these two quantitative parameters in pathological grading of breast invasive ductal carcinoma were compared.Results The mean,25 %,50 %,75 %,90 % percentiles,kurtosis and skewness of RRKtrans in high grade group were (0.793±0.258)/min,(0.484±0.209)/min,(0.773±0.277)/min,(1.066±0.351)/min,(1.322±0.406)/min,2.647 (1.426,3.679),0.398 (0.297,0.514) respectively,and the corresponding parameters in low grade group were (0.506±0.203)/min,(0.301 ±0.142)/min,(0.487 ±0.211)/min,(0.692±0.281)/min,(0.861±0.323)/min,1.725 (0.779,2.316),0.258 (0.133,0.302) respectively.There were significant differences between the two groups (all P 〈 0.05).The mean,50 %,75 %,90 %percentiles of Ktrans in high grade groups were (0.099±0.034)/min,(0.110±0.033)/min,(0.132±0.045)/min,(0.140±0.047)/min respectively,and the corresponding parameters in low grade group were (0.067±0.030)/min,(0.082 ±0.067)/min,(0.096 ±0.059)/min,(0.113 ±0.074)/min respectively.There were significant differences between the two groups (all P 〈 0.05).RRKtrans was superior to Ktrans in distinguishing area under the curve (AUC) of receiver operating characteristic curve (ROC) of high and low grading of breast invasive ductal carcinoma.Conclusion RRK^trans obtained by Reference Region model and K^trans obtained by Population AIF DCE-MRI have some values in pathological grading of breast invasive ductal carcinoma,but the performance of RRK^trans is superior to K^ftrans.
作者 马翠萍 徐成 高强 石祥呈 史中强 Ma Cuiping;Xu Cheng;Gao Qiang;Shi Xiangcheng;Shi Zhongqiang(Department of Radiology,MRI Room,Shanxi Provincial People' s Hospital,Taiyuan 030012,China(Ma CP,Xu C,Gao Q;Department of Pathology,Shanxi Provincial People' s Hospital,Taiyuan 030012,China(Shi XC;General Electric Pharmaceutical(Shanghai)Co.LTD,Shanghai 201203,China(Shi ZQ)
出处 《肿瘤研究与临床》 CAS 2018年第7期468-472,476,共6页 Cancer Research and Clinic
关键词 乳腺肿瘤 动态对比增强磁共振成像 参照物模型 动脉输入函数 Breast neoplasms Dynamic contrast-enhanced magnetic resonance imaging Reference region model Arterial input function
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