乳腺癌骨转移风险及其预测模型的构建与验证

Construction and validation of risk prediction model for breast cancer bone metastasis

目的筛选乳腺癌骨转移发生的风险因素并构建一个预测它发生的预测模型。方法从SEER*Stat(版本8.4.1)数据库中获取2010–2015年期间符合纳入和排除标准的乳腺癌患者数据,同时搜集2021–2023年期间西南医科大学附属医院诊断为远处转移的乳腺癌患者。使用R软件将SEER数据库中的患者按7∶3随机分为训练集及验证集,其中西南医科大学附属医院的68例乳腺癌远处转移患者也纳入验证集。采用单因素和多因素logistic回归分析与乳腺癌发生骨转移的危险因素并据此建立列线图预测模型,采用受试者操作特征曲线下面积(area under receiver operating characteristic curve,AUC)、校准曲线、决策曲线分析进一步评价列线图预测模型的效能。结果本研究共纳入8637例乳腺癌患者,其中训练集5998例,验证集2639例(包括从临床病例中纳入的68例乳腺癌患者),训练集和验证集除了在种族和N分期方面比较差异有统计学意义(P<0.05)外,二者在其余临床病理特征方面比较差异无统计学意义(P>0.05)。多因素logistic回归分析结果显示,患者的种族为白种人、组织学分级低(Ⅰ~Ⅱ级)、雌和孕激素受体状态阳性、人表皮生长因子受体2状态阴性、乳腺原发灶部位未进行手术会增加乳腺癌发生骨转移的风险(P<0.05),以这些风险因素构建的列线图在训练集和验证集中的AUC(95%CI)分别为0.676(0.533,0.744)和0.690(0.549,0.739),通过Bootstrap采样对列线图进行1000次内部校准显示,训练集与验证集的校准曲线均接近理想的45°参考线,训练集与验证集的决策曲线分析显示在一定阈值概率范围内有较强的临床实用性。结论本研究以与乳腺癌骨转移风险有关的因素如种族、组织学分级、雌和孕激素受体以及人表皮生长因子受体2、乳腺原发灶部位是否手术构建的列线图预测模型在训练集和验证集中的实际结果与预测结果比较一致,有一定的准确性,同时也体现了列线图有较强的临床实用性,但也需要看到列线图在训练集和验证集中对乳腺癌骨转移的区分度一般,其原因需要进一步分析。

Objective To identify the risk factors of bone metastasis in breast cancer and construct a predictive model.Methods The data of breast cancer patients met inclusion and exclusion criteria from 2010 to 2015 were obtained from the SEER*Stat database.Additionally,the data of breast cancer patients diagnosed with distant metastasis in the Affiliated Hospital of Southwest Medical University from 2021 to 2023 were collected.The patients from the SEER database were randomly divided into training(70%)and validation(30%)sets using R software,and the breast cancer patients from the Affiliated Hospital of Southwest Medical University were included in the validation set.The univariate and multivariate logistic regressions were used to identify risk factors of breast cancer bone metastasis.A nomogram predictive model was then constructed based on these factors.The predictive effect of the nomogram was evaluated using the area under the receiver operating characteristic curve(AUC),calibration curve,and decision curve analysis.Results The study included 8637 breast cancer patients,with 5998 in the training set and 2639(including 68 patients in the Affiliated Hospital of Southwest Medical University)in the validation set.The statistical differences in the race and N stage were observed between the training and validation sets(P<0.05).The multivariate logistic regression analysis revealed that being of white race,having a low histological grade(Ⅰ–Ⅱ),positive estrogen and progesterone receptors status,negative human epidermal growth factor receptor 2 status,and non-undergoing surgery for the primary breast cancer site increased the risk of breast cancer bone metastasis(P<0.05).The nomogram based on these risk factors showed that the AUC(95%CI)of the training and validation sets was 0.676(0.533,0.744)and 0.690(0.549,0.739),respectively.The internal calibration using 1000 Bootstrap samples demonstrated that the calibration curves for both sets closely approximated the ideal 45-degree reference line.The decision curve analysis indicated a stronger clinical utility within a certain probability threshold range.Conclusions This study constructs a nomogram predictive model based on factors related to the risk of breast cancer bone metastasis,which demonstrates a good consistency between actual and predicted outcomes in both training and validation sets.The nomogram shows a stronger clinical utility,but further analysis is needed to understand the reasons of the lower differentiation of nomogram in both sets.