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乳腺癌病因复杂,病人预后欠佳。癌细胞发生转移是影响肿瘤病人预后的重要因素[1]。腋窝淋巴结(axillary lymph node, ALN)是乳腺癌常见的转移路径。前哨淋巴结活检(sentinel lymph node biopsy, SLNB)是临床资料阴性病人腋窝淋巴结清扫(axillary lymph node dissection, ALND)的依据,但两者均易导致病人发生上肢麻木、肩部疼痛、局部水肿等不适症状。研究报道,40%~70%的早期乳腺癌病人并未发生ALN转移[2],或仅前哨淋巴结受累,若直接对这些病人行ALND,则属过度治疗,易引起争议。但因ALN转移而未采取ALND,会严重影响预后。因而本研究基于多维度指标分析,筛选出危险因素用于构建乳腺癌病人ALN转移的预测模型,以便区分哪类病人适合SLNB手术或ALND,降低非必要的淋巴结活检术或ALND带给病人的创伤与并发症。
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转移组和非转移组在肿瘤边缘模糊、ALN皮质厚度、ALN短径、ALN短径/长径的比值、分级程度、miRNA-203表达量方面的比较,差异均有统计学意义(P < 0.05~P < 0.01),其余指标差异均无统计学意义(P>0.05)(见表 1)。
临床资料 转移组
(n=66)非转移组
(n=176)t P 年龄/岁 54.63±5.94 53.46±5.75 1.39 >0.05 已绝经 29 61 1.77* >0.05 肿瘤最大直径/cm 3.65±1.14 3.41±1.03 1.57 >0.05 肿瘤边缘模糊 32 49 9.19* < 0.05 肿瘤形态不规则 41 92 1.88* >0.05 肿瘤回声不均 44 96 3.16* >0.05 ALN皮质厚度/mm 3.32±0.79 1.83±0.42 14.57# < 0.01 ALN长径/mm 14.18±3.75 13.63±3.74 1.02 >0.05 ALN短径/mm 8.72±1.31 5.23±1.35 18.05 < 0.01 ALN短径/长径 0.61±0.17 0.38±0.09 10.46# < 0.01 临床分期 Ⅰ期 37 114 1.55* >0.05 Ⅱ期 29 62 分级程度 Ⅲ级 24 37 2.55△ < 0.05 Ⅱ级 31 91 Ⅰ级 11 48 ER阳性 23 45 2.05* >0.05 PR阳性 26 59 0.73* >0.05 Her-2阳性 25 51 1.77* >0.05 Ki-67阳性 32 66 2.40* >0.05 CA153/(U/mL) 29.24±5.28 28.32±4.36 1.38 >0.05 CAE/(ng/mL) 7.31±2.15 6.83±2.08 1.25 >0.05 WBC/(×109/L) 6.94±1.48 6.77±1.42 0.82 >0.05 PLT/(×109/L) 237.63±62.47 230.58±60.15 0.80 >0.05 D-二聚体/(μg/L) 184.56±46.73 179.75±41.87 0.75 >0.05 白蛋白/(g/L) 29.48±6.84 30.86±7.32 1.31 >0.05 miRNA-203表达量 4.53±0.87 4.07±0.56 4.00# < 0.01 *示χ2值; #示t′值; △示zc值 表 1 临床资料的比较(x±s,n)
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以病理确诊是否(0=否;1=是)存在ALN转移为因变量,将表 1分析P < 0.05的特征为自变量,分类资料赋值[肿瘤边缘模糊(0=否;1=是)、肿瘤的分级程度(0=Ⅰ级;1=Ⅱ级;2=Ⅲ级)],计量资料原值录入。经多因素logistic回归分析表明,肿瘤边缘模糊、ALN皮质厚度厚、ALN短径短、ALN短径/长径的比值大、分级程度高、miRNA-203表达量高均是乳腺癌病人ALN转移的危险因素(P < 0.05~P < 0.01)(见表 2)。
变量 B SE Waldχ2 P OR(95%CI) 肿瘤边缘模糊 0.648 0.275 5.55 < 0.05 1.912(1.273~3.508) ALN皮质厚度 1.026 0.351 8.54 < 0.05 2.789(1.615~4.954) ALN短径 0.824 0.297 7.70 < 0.05 2.280(1.473~3.165) ALN短径/长径比值 1.328 0.472 7.92 < 0.05 3.773(2.354~6.281) 分级程度 0.742 0.246 8.66 < 0.05 2.101(1.549~3.087) miRNA-203表达量 1.261 0.405 9.69 < 0.05 3.529(2.187~5.942) 常数项 -30.724 8.436 13.264 < 0.01 — 表 2 多因素logistic分析结果
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以表 2回归系数和常数项构建乳腺癌病人ALN转移的预测模型,得出预测模型方程为:预测概率Prob =1/(e^-Y),Y=-30.724+0.648×肿瘤边缘模糊(0=否;1=是)+ 1.026×ALN皮质厚度(实际值)+0.824×ALN短径(实际值)+1.328×ALN短径/长径的比值(实际值)+ 0.742×肿瘤的分级程度(0=Ⅰ级,1=Ⅱ级,2=Ⅲ级)+ 1.261×miRNA-203表达量(实际值)。将模型的预测概率值作为检验变量,以是否发生ALN转移为状态变量。绘制ROC评价预测模型区分度,结果提示ROC曲线下AUC面积为0.889(95%CI: 0.840~0.934),以最大约登指数(0.679)计算出模型阈值为0.504,对应灵敏度和特异度分别为88.62%、83.92%,说明模型区分度较好(见图 1);经拟合优度检验(χ2=2.06,P>0.05),表明模型不存在过拟合现象(见图 2)。
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选择2020年7月至2022年3月我院收治的93例单侧乳腺癌女性病人作为预测模型的临床验证。以病理结果为“金标准”,验证模型效能,结果模型预测的灵敏度为88.46%(23/26),特异度为83.58%(56/67),准确率为84.95%(79/93)(见表 3)。
模型预测结果 实际结果 合计 ALN转移 ALN未转移 ALN转移 23 11 34 ALN未转移 3 56 59 合计 26 67 93 表 3 预测模型的临床验证结果
基于多维度指标建立预测模型在乳腺癌病人腋窝淋巴结转移的应用价值
Application value of prediction model based on multi-dimensional indicators in axillary lymph node metastasis in breast cancer patients
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摘要:
目的探讨以多维度指标建立预测模型在乳腺癌病人腋窝淋巴结(ALN)转移的应用价值。 方法回顾性选取242例单侧乳腺癌女性病人作为研究对象。以病理检测结果确认有无ALN转移,划分为转移组(n=66)和非转移组(n=176)。对比2组的临床资料,用多因素logistic回归分析乳腺癌病人ALN转移的危险因素并建立预测模型。应用ROC曲线分析模型的区分度,应用拟合优度检验模型的校准度。另选93例单侧乳腺癌女性病人用于模型的临床验证。 结果多因素logistic回归分析显示,肿瘤边缘模糊(OR=1.912)、ALN皮质厚度厚(OR=2.789)、ALN短径短(OR=2.280)、ALN短径/长径的比值大(OR=3.773)、分级程度高(OR=2.101)、miRNA-203表达量高(OR=3.529)是乳腺癌病人ALN转移的危险因素(P < 0.05~P < 0.01)。根据危险因素得出预测模型方程:Prob =1/(e^-Y),Y=-30.724+0.648×肿瘤边缘模糊+ 1.026×ALN皮质厚度+ 0.824×ALN短径+1.328×ALN短径/长径的比值+ 0.742×分级程度+ 1.261×miRNA-203表达量。ROC曲线分析显示模型预测乳腺癌病人ALN转移的AUC面积为0.889(95%CI: 0.840~0.934),说明模型区分度较好;拟合优度检验χ2=2.06,P>0.05,说明模型无过拟合现象。预测模型在临床验证得到的灵敏度为88.46%、特异度为83.58%、准确率为84.95%。 结论以血清miRNA-203表达量、肿瘤分级程度、肿瘤边缘及ALN的皮质厚度、短径、短径/长径的比值来构建的乳腺癌病人ALN转移的预测模型有一定价值。 Abstract:ObjectiveTo explore the application value of establishing a prediction model based on multi-dimensional indicators in axillary lymph node(ALN) metastasis in breast cancer patients. MethodsA total of 242 female patients with unilateral breast cancer were retrospectively selected as the research subjects.The presence or absence of ALN metastasis were confirmed by pathological test results.Patients were divided into metastatic group(n=66) and non-metastatic group(n=176).The clinical data of two groups were compared.Multivariate logistic regression analysis was used to analyze the risk factors of ALN metastasis in breast cancer patients and establish a prediction model.ROC curve was used to analyze the discrimination of the model, and goodness of fit was used to test the calibration of the model.Another 93 female patients with unilateral breast cancer were selected for clinical validation of the model. ResultsMultivariate logistic regression analysis showed that blurred tumor margin(OR=1.912), thick ALN cortex(OR =2.789), short ALN diameter(OR=2.280), large ratio of short ALN diameter to long ALN diameter(OR=3.773), high grade of pathological(OR=2.101), and high expression of miRNA-203(OR=3.529) were risk factors for ALN metastasis in breast cancer patients(P < 0.05 to P < 0.01).The prediction model equation derived from the risk factors was Prob =1/(e^-Y), Y=-30.724+0.648×tumor margin blur+1.026×ALN cortical thickness+0.824×ALN short diameter+1.328×ALN short diameter/long diameter ratio+0.742×pathological grade +1.261×miRNA-203 expression level.ROC curve analysis showed that the AUC area of ALN metastasis in breast cancer patients predicted by the model was 0.889(95%CI: 0.840-0.934), indicating that the model had good discrimination.Goodness of fit test was χ2=2.06, P>0.05, indicating that the model had no overfitting phenomenon.The sensitivity, specificity and accuracy of the prediction model were 88.46%, 83.58% and 84.95%, respectively. ConclusionsThe prediction model of ALN metastasis in breast cancer patients constructed by serum miRNA-203 expression, tumor pathological grade, tumor margin and cortical thickness of ALN, short diameter, and short diameter/long diameter ratio has certain value. -
Key words:
- breast neoplasms /
- axillary lymph node metastasis /
- risk factors /
- prediction model
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表 1 临床资料的比较(x±s,n)
临床资料 转移组
(n=66)非转移组
(n=176)t P 年龄/岁 54.63±5.94 53.46±5.75 1.39 >0.05 已绝经 29 61 1.77* >0.05 肿瘤最大直径/cm 3.65±1.14 3.41±1.03 1.57 >0.05 肿瘤边缘模糊 32 49 9.19* < 0.05 肿瘤形态不规则 41 92 1.88* >0.05 肿瘤回声不均 44 96 3.16* >0.05 ALN皮质厚度/mm 3.32±0.79 1.83±0.42 14.57# < 0.01 ALN长径/mm 14.18±3.75 13.63±3.74 1.02 >0.05 ALN短径/mm 8.72±1.31 5.23±1.35 18.05 < 0.01 ALN短径/长径 0.61±0.17 0.38±0.09 10.46# < 0.01 临床分期 Ⅰ期 37 114 1.55* >0.05 Ⅱ期 29 62 分级程度 Ⅲ级 24 37 2.55△ < 0.05 Ⅱ级 31 91 Ⅰ级 11 48 ER阳性 23 45 2.05* >0.05 PR阳性 26 59 0.73* >0.05 Her-2阳性 25 51 1.77* >0.05 Ki-67阳性 32 66 2.40* >0.05 CA153/(U/mL) 29.24±5.28 28.32±4.36 1.38 >0.05 CAE/(ng/mL) 7.31±2.15 6.83±2.08 1.25 >0.05 WBC/(×109/L) 6.94±1.48 6.77±1.42 0.82 >0.05 PLT/(×109/L) 237.63±62.47 230.58±60.15 0.80 >0.05 D-二聚体/(μg/L) 184.56±46.73 179.75±41.87 0.75 >0.05 白蛋白/(g/L) 29.48±6.84 30.86±7.32 1.31 >0.05 miRNA-203表达量 4.53±0.87 4.07±0.56 4.00# < 0.01 *示χ2值; #示t′值; △示zc值 表 2 多因素logistic分析结果
变量 B SE Waldχ2 P OR(95%CI) 肿瘤边缘模糊 0.648 0.275 5.55 < 0.05 1.912(1.273~3.508) ALN皮质厚度 1.026 0.351 8.54 < 0.05 2.789(1.615~4.954) ALN短径 0.824 0.297 7.70 < 0.05 2.280(1.473~3.165) ALN短径/长径比值 1.328 0.472 7.92 < 0.05 3.773(2.354~6.281) 分级程度 0.742 0.246 8.66 < 0.05 2.101(1.549~3.087) miRNA-203表达量 1.261 0.405 9.69 < 0.05 3.529(2.187~5.942) 常数项 -30.724 8.436 13.264 < 0.01 — 表 3 预测模型的临床验证结果
模型预测结果 实际结果 合计 ALN转移 ALN未转移 ALN转移 23 11 34 ALN未转移 3 56 59 合计 26 67 93 -
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