基于扩散加权成像和动态增强MRI的影像组学特征与乳腺癌分子分型的关系初探

Correlation of radiomic features based on diffusion weighted imaging and dynamic contrast-enhancement MRI with molecular subtypes of breast cancer

目的 探讨基于DWI和动态增强MRI（DCE-MRI）的影像组学特征与乳腺癌分子分型的关系.方法 回顾性分析广东省人民医院2015年6月至2016年6月,经手术病理证实为单发肿块型乳腺癌,获得乳腺癌分子分型,且行乳腺MRI扫描并获得DCE-MRI及ADC图像的79例患者.记录乳腺病灶MRI传统定量指标,包括ADC值和初始强化率（IER）;对ADC图和DCE-MRI图上的病灶区进行手动分割并提取影像组学特征,降维后筛选出10个影像组学标签.对病理标本进行免疫组织化学检测,分为Luminal A型、Luminal B型、人表皮生长因子受体2（HER2）过表达型、三阴性（TN）型乳腺癌.采用单因素logistic回归分析,比较ADC值、IER值以及影像组学标签独立进行分子分型预测的效果;采用多因素logistic回归建模,并绘制ROC,计算ROC下面积（AUC）,比较各模型的诊断效能;采用Hosmer-Lemeshow检验对模型的拟合优度进行检验.结果 Luminal A型29例,Luminal B型39例, HER2过表达型5例,TN型6例.采用单因素logistic回归分析法对传统乳腺MRI参数ADC、IER值及所提取的10个影像组学标签在进行分子分型分类中的效果进行分析,ADC、IER值在鉴别各组的分子分型时,AUC值均〈0.70（0.516-0.605）,鉴别价值不大;鉴别各个分子分型时,至少有1个影像特征AUC〉0.70,其中DCE_L_G_2.5_autocorrelation鉴别TN的AUC最高（0.941）.行多因素logistic回归分析,获得了鉴别诊断的最佳模型,鉴别Luminal A和非Luminal A型、Luminal B和非Luminal B型、TN和非TN的最佳模型鉴别诊断的AUC分别为0.786、0.733和0.941,经Hosmer-Lemeshow检验,各模型P均〉0.10（分别为0.156、0.204和0.820）,说明所建立的各个模型的预测值与观测值之间差异无统计学意义,模型拟合效果较好.结论 基于DWI和DCE-MRI的影像组学特征则有助于鉴别乳腺癌的分子亚型,尤其是鉴别TN型乳腺癌具有较大价值.

Objective To explore the relationship between radiomics signatures based on DWI and dynamic contrast-enhanced MRI （DCE-MRI） and molecular subtypes of breast cancer.Methods A retrospective analysis of 79 female breast cancer patients, with single mass, clear molecular subtypes and preoperative breast MRI scanning （obtaining DCE-MRI and ADC images）, of Guangdong General Hospital from June 2015 to June 2016,were performed.Traditional quantitative parameters,including ADC value and initial enhancement rate（IER）,were recorded.Texture analysis were performed on ADC map and DCE map, with manual segmentation and extraction of radiomic features,and Manual segmentation was performed on ADC map and DCE map, radiomics features were extracted and 10 radiomics signatures were finally selected after dimension reduction. Four molecular subtypes of breast cancer were classified by immunohistochemical detection of pathological specimens, including Luminal A, Luminal B, human epidermal growth factor receptor 2 （HER2） overexpression and triple negative （TN）. Univariate logistic regression analysis was used for assessing the performance of ADC values, IER values and radiomics signatures to independently predict molecular subtypes groups.Multivariate logistic regression analysis was performed to establish predicting models, then receiver operating characteristic curves （ROC） were drawn and areas under ROC curve were calculated to compare the diagnostic performance of each model. The Hosmer-Lemeshow test was performed to test the goodness of model fitness. Results There were 29 cases of Luminal A, 39 cases of Luminal B, 5 cases of HER2 overexpression and 6 cases of TN breast cancer patients.Univariate logistic regression analysis was used to assess the ability of traditonal MRI parameters of ADC and IER values and ten of the radiomics siganitures in classifying molecular subtypes,results showed that the AUC values of ADC and IER values, were both less than 0.70 （range 0.516 to 0.605）, which indicated valueless;at least one radiomic signature had AUC greater than 0.70 when identifying each molecular subtype, and AUC of DCE_L_G_2.5_autocorrelation achieved the highest value of 0.941 in identifying TN and non-TN subtypes.Multivariate logistic regression analysis were performed to obtain the best model, results showed that the AUCs for classifying Luminal A and non-Luminal A, Luminal B and non-Luminal B, TN and non-TN subtypes were 0.786 and 0.733 And 0.941, respectively. The Hosmer-Lemeshow test showed that the P values of all models were larger than 0.10 （0.156, 0.204 and 0.820,respectively）,indicating that there was no significant difference between the predicted and observed values of each model established, these models were all fitted good. Conclusion The radiomics features based on ADC map and DCE map can help to identify the molecular subtypes of breast cancer,especially for the identification of TN type breast cancer.