外周血CK19 mRNA联合超声造影构建可手术乳腺癌腋窝淋巴结转移预测模型  

CK19 mRNA in peripheral blood combined with contrast-enhanced ultrasound: the predictive value of axillary lymph node involvement in breast cancer

作　　者：俞星飞[1] 周玲燕[2] 杨琛[1] 陈波[3] 王晨[1] 梁晨露 宣浩军 张喜平[1] 陈道宝[1] 俞洋[1] 黄建 杨红健[1] Yu Xingfei;Zhou Lingyan;Yang Chen;Chen Bo;Wang Chen;Liang Chenlu;Xuan Haojun;ZhangXiping;Chen Daobao;Yu Yang;Huang Jian;Yang Hongjian(Breast Tumor Surgery, Zhejiang Cancer Hospital, Hangzhou 310022, China;Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China;Department of Pathology, Zhejiang Cancer Hospital, Hangzhou 310022, China;Department of Surgical Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China)

机构地区：[1]浙江省肿瘤医院乳腺外科,杭州310022 [2]浙江省肿瘤医院超声科,杭州310022 [3]浙江省肿瘤医院病理科,杭州310022 [4]浙江大学附属第二医院肿瘤中心,杭州310009

出　　处：《中华普通外科杂志》2019年第6期483-488,共6页Chinese Journal of General Surgery

基　　金：浙江省自然科学基金(Y18H160152);浙江省中医药科技计划项目(2017ZA030).

摘　　要：目的评估联合术前外周血CK19 mRNA和超声造影评分构建可手术乳腺癌腋淋巴结转移预测系统的可行性并验证其预测效能。方法入组可手术初治接受前哨淋巴结活检的乳腺癌病例,设训练集和验证集。术前RT-PCR方法检测外周血CK19mRNA表达,并行患侧腋窝淋巴结超声造影评分。在训练集中以术后前哨淋巴结(SLN)和非前哨淋巴结(nSLN)病理结果为金标准,筛选有效的建模变量,采用Logistic回归建立预测模型,并在验证集中与MSKCC模型进行平行对照研究,评估预测准确性和预测效能。结果2015年10月至2016年11月连续纳入符合入组标准的359例病例(训练集),分别建立对SLN和nSLN(SLN阳性时)的预测数学公式。预测SLN转移的敏感度91.36%,特异度 94. 92%, AUC 0. 979;预测 nSLN 转移的敏感度 91.04%,特异度 90. 53%, AUC0. 932;2016年12月至2017年7月连续纳入符合入组标准的219例(验证集),模型预测SLN转移的敏感度91. 84%,特异度96. 69%,AUC为0. 979,显著优于MSKCC模型(0. 739);预测nSLN转移的敏感度95.35%,特异度92. 73%,AUC为0. 945 ,显著优于MSKCC模型(0.873)。结论通过联合外周血CK19 mRNA和超声造影评分建立可手术乳腺癌腋淋巴结转移预测系统,可计算得到SLN/nSLN转移概率并可进行定性判断,该模型总体准确性和AUC显著优于MSKCC预测模型。Objective To evaluate a model for axillary lymph node involvement combining CK19 mRNA with contrast enhanced ultrasound sonography ( CEUS) score in operable breast cancer. Methods Operable breast cancer patients planned for sentinel lymph node ( SLN) biopsy were enrolled. Preoperative CK19mRNA expressions in peripheral blood and CEUS score of axillary lymph nodes were tested before surgery. In the training set, postoperative sentinel lymph node ( SLN) and non-sentinel lymph node ( nSLN) pathological results were taken as the gold standard, effective modeling variables were screened, logistic regression was used to establish the prediction model. Parallel control studies were conducted between the validation set and the MSKCC model to evaluate the prediction accuracy and prediction efficiency. Results From Oct 2015 to Nov 2016, 359 cases (training set) were enrolled and mathematical formulas for predicting SLN and nSLN were established, respectively. The sensitivity, specificity and AUC of predicting SLN involvement were 91.36%, 94. 92% and 0. 979 respectively. The sensitivity, specificity and AUC of predicting nSLN metastasis were 91.04%, 90. 53% and 0. 932 respectively. From Dec 2016 to Jul 2017, 219 cases ( verification set) were included. The sensitivity of SLN metastasis predicted by the model was 91. 84%, the specificity was 96. 69%, and the AUC was 0. 979, significantly superior to the MSKCC model (0. 739). The sensitivity, specificity and AUC of predicting nSLN metastasis were 95. 35%, 92. 73% and 0. 945 respectively, significantly superior to the MSKCC model (0. 873 ). Concolusions Combined with peripheral blood CK19 mRNA and CEUS score, the prediction model for axillary lymph node involvement for operable breast cancer, SLN/nSLN involvement probability can be calculated and qualitative judgment can be made. The overall accuracy and AUC of this model are better than the prediction model of MSKCC.

关 键 词：乳腺肿瘤 RNA 信使 超声造影 淋巴结转移 预测 

分 类 号：R737.9[医药卫生—肿瘤]