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基于超声特征联合临床资料的预测模型对早期乳腺癌患者腋窝淋巴结转移的评估价值 被引量:9

Value of prediction model established based on ultrasound features combined with clinical data in evaluating axillary lymph node metastasis in patients with early breast cancer
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摘要 目的 探讨基于超声特征联合临床资料建立的预测模型对早期乳腺癌患者腋窝淋巴结(ALN)转移的评估价值。方法 回顾性分析203例单侧早期乳腺癌女性患者的超声特征和临床资料,根据患者病理结果判定是否发生ALN转移并将患者分为转移组和非转移组。先对2组患者各项指标进行单因素分析,再行多因素Logistic回归分析,建立预测模型。应用受试者工作特征(ROC)曲线检测模型的区分度,应用拟合优度检验评价模型的校准度,另选取78例单侧早期乳腺癌患者对该模型进行临床验证。结果 203例乳腺癌患者的病理结果显示,71例患者出现ALN转移(转移组),占34.98%。肿瘤直径≥3 cm、肿瘤边缘模糊、ALN短径较长、ALN短径与长径比值(短径/长径)较高、ALN皮质厚度较厚、分化程度低、血清微小RNA-21(miRNA-21)表达量高均为早期乳腺癌患者ALN转移的危险因素(P<0.05)。根据危险因素建立预测模型,方程为Logit(P)=1.912×肿瘤直径≥3 cm(是=1,否=0)+2.040×肿瘤边缘模糊(是=1,否=0)+1.582×ALN短径(实测值)+3.374×ALN短径/长径(实测值)+2.264×ALN皮质厚度(实测值)+2.497×分化程度(高分化=0,中分化=1,低分化=2)+2.921×miRNA-21表达量(实测值)-33.615。ROC曲线分析结果显示,该模型的曲线下面积为0.886(95%CI:0.838~0.933),最大约登指数(0.736)对应的灵敏度、特异度分别为88.50%、83.60%。拟合优度检验显示,该模型不存在过拟合现象(χ^(2)=2.067,P=0.394)。临床验证结果显示,该模型的灵敏度为87.10%,特异度为82.98%,准确度为84.62%。结论 基于肿瘤分化程度、血清miRNA-21表达量、肿瘤直径、肿瘤边缘以及ALN的短径、短径/长径、皮质厚度指标建立的预测模型对早期乳腺癌患者ALN转移风险具有较好的评估价值。 Objective To explore value of prediction model established based on ultrasound features combined with clinical data in evaluating axillary lymph node(ALN) metastasis in patients with early breast cancer.Methods The ultrasonic characteristics and clinical data of 203 women with unilateral early breast cancer were retrospectively analyzed.The patients were divided into metastatic group and non-metastatic group,and were determined whether they had ALN metastasis or not according to the pathological results.Single factor screening was performed for each index of the two groups.Logistic multivariate regression analysis was performed again and a prediction model was established.Receiver operating characteristic(ROC) curve was used to detect its discrimination,and goodness-of-fit test was used to evaluate the degree of calibration.Another 78 unilateral early breast cancer patients in our hospital were selected for clinical validation of the model.Results The pathological results of 203 breast cancer patients showed that ALN metastasis occurred in 71 cases(metastasis group),accounting for 34.98%.Tumor diameter ≥3 cm,blurred tumor margin,longer ALN short diameter,higher ALN short diameter to long diameter ratio,higher value of ALN cortical thickness,lower degree of differentiation,and higher level of serum microRNA-21(miRNA-21) expression were all risk factors for ALN metastasis in breast cancer patients(P<0.05).According to the risk factors,the prediction model expression equation was as follows.Logit(P)=1.912 × tumor diameter ≥3 cm(yes =1,no=0) + 2.040 × tumor margin blur(yes=1,no=0) + 1.582 × ALN short diameter(measured value) + 3.374 × ALN short/long diameter(measured value) + 2.264 × ALN cortical thickness(measured value) + 2.497 × differentiation degree(yes=1,no=0) + 2.921 × miRNA-21 expression amount(measured value)-33.615.The area of the ROC curve of this model was 0.886(95% CI,0.838 to 0.933),the sensitivity and specificity corresponding to the maximum Youden index(0.736) were88.50% and 83.60% respectively.Goodness of fit test showed that the model did not overfit(χ^(2)=2.067,P=0.394).Clinical validation results showed that the sensitivity of the model was 87.10%,the specificity was 82.98%,and the accuracy was 84.62%.Conclusion It is valuable in predicting the risk of ALN metastasis by constructing a predictive model based on degree of tumor differentiation,serum miRNA-21 expression,tumor diameter,tumor margin,and ALN short-diameter,short-diameter/long-diameter ratio,and cortical thickness in early breast cancer patients.
作者 熊朝月 周敏 何小芳 华骁帆 XIONG Chaoyue;ZHOU Min;HE Xiaofang;HUA Xiaofan(Department of Ultrasound,Suzhou Ninth People′s Hospital of Jiangsu Province,Suzhou,Jiangsu,215200;Department of General Surgery,Suzhou Ninth People′s Hospital of Jiangsu Province,Suzhou,Jiangsu,215200)
出处 《实用临床医药杂志》 CAS 2022年第12期14-18,22,共6页 Journal of Clinical Medicine in Practice
关键词 乳腺癌 腋窝淋巴结转移 超声特征 临床资料 预测模型 肿瘤分化程度 breast cancer axillary lymph node metastasis ultrasound features clinical data prediction model degree of tumor differentiation
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