目的 预测初诊单纯骨转移乳腺癌(bone-only metastatic breast cancer,bMBC)患者局部手术的主要获益人群.方法 使用SEER数据库中2010年-2019年初诊为bMBC的病例资料,按7∶3将患者随机分为训练集与验证集.在训练集中采用Cox比例风险模型分析患者总生存的独立预后因素,筛选变量并构建预后预测模型.采用一致性指数(concordance index,C-index)、时间依赖临床受试者操作特征曲线及曲线下面积(area under the curve,AUC)、校准曲线和决策曲线分析(decision curve analysis,DCA)分别在训练集和验证集中评估模型的区分度、校准度和临床适用度.使用模型计算患者风险得分并将患者分为低、中、高风险组,使用生存分析比较手术与未手术患者在不同风险组中的生存差异.结果 共纳入2057例患者,中位年龄45岁(四分位间距47～62岁),中位随访32个月(四分位间距16～53个月),共死亡865例(42.1％).多因素Cox比例风险模型分析结果显示手术较未手术患者的总生存更优[风险比=0.51,95％置信区间(0.43,0.60),P＜0.001],而化疗、婚姻状态、分子分型、年龄、病理类型及组织学分级是总生存独立预后因素(P＜0.05),基于独立预后因素构建预后预测模型.在训练集和验证集进行模型评价,C-index分别为0.702和0.703;训练集和验证集的1、3、5年AUC分别为0.734、0.727、0.731和0.755、0.737、0.708;校准曲线显示训练集和验证集1、3、5年的预测生存率与实际生存率高度吻合;DCA显示预测模型在训练集和验证集具有一定临床适用度.根据患者风险得分将患者划分为低、中、高风险亚组,对数秩检验结果显示,局部手术能提高低风险组患者总生存(训练集:P=0.013;验证集:P=0.024),局部手术不改善中风险组(训练集:P=0.45;验证集:P=0.77)和高风险组(训练集:P=0.56;验证集:P=0.94)患者总生存.结论 局部手术可提高部分初诊bMBC患者的总生存,基于临床病理特征构建的预测模型对患者进行预后分层能评估初诊bMBC患者中局部手术的获益情况.
Construction and validation of a predicting model for benefit from local surgery for bone-only metastatic breast cancer:a retrospective study based on SEER database
Objective To predict the patients who can benefit from local surgery for bone-only metastatic breast cancer(bMBC).Methods Patients newly diagnosed with bMBC between 2010 and 2019 in SEER database were randomly divided into a training set and a validation set at a ratio of 7∶3.The Cox proportional hazards model was used to analyze the independent prognostic factors of overall survival in the training set,and the variables were screened and the prognostic prediction model was constructed.The concordance index(C-index),time-dependent clinical receiver operating characteristic curve and area under the curve(AUC),calibration curve and decision curve analysis(DCA)were used to evaluate the discrimination,calibration and clinical applicability of the model in the training set and validation set,respectively.The model was used to calculate the patient risk score and classify the patients into low-,medium-and high-risk groups.Survival analysis was used to compare the survival difference between surgical and non-surgical patients in different risk groups.Results A total of 2 057 patients were enrolled with a median age of 45 years(interquartile range 47-62 years)and a median follow-up of 32 months(interquartile range 16-53 months).Totally 865 patients(42.1％)died.Multivariate Cox proportional hazards model analysis showed that the overall survival of patients with surgery was better than that of patients without surgery[hazard ratio=0.51,95％confidence interval(0.43,0.60),P＜0.001].Chemotherapy,marital status,molecular subtype,age,pathological type and histological grade were independent prognostic factors for overall survival(P＜0.05),and a prognostic prediction model was constructed based on the independent prognostic factors.The C-index was 0.702 in the training set and 0.703 in the validation set.The 1-,3-,and 5-year AUCs of the training set and validation set were 0.734,0.727,0.731 and 0.755,0.737,0.708,respectively.The calibration curve showed that the predicted survival rates of 1,3,and 5 years in the training set and the validation set were highly consistent with the actual survival rates.DCA showed that the prediction model had certain clinical applicability in the training set and the validation set.Patients were divided into low-,medium-and high-risk subgroups according to their risk scores.The results of log-rank test showed that local surgery improved overall survival in the low-risk group(training set:P=0.013;validation set:P=0.024),but local surgery did not improve overall survival in the medium-risk group(training set:P=0.45;validation set:P=0.77)or high-risk group(training set:P=0.56;validation set:P=0.94).Conclusions Local surgery can improve the overall survival of some patients with newly diagnosed bMBC.The prognostic stratification model based on clinicopathological features can evaluate the benefit of local surgery in patients with newly diagnosed bMBC.
Breast cancerbone metastasissurgeryprognosisprediction modelSEER database
NETL
NSTL
万方数据
