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基于SEER数据库构建初诊Ⅳ期乳腺癌手术获益的预测模型与验证OA北大核心CSTPCD

Development and validation of a prediction model for surgical benefit in patients with de novo metastatic breast cancer based on SEER database

中文摘要英文摘要

背景与目的:外科手术对初诊Ⅳ期乳腺癌(dnMBC)患者的预后价值一直存有争议,部分患者能从局部手术治疗中获益,但目前尚缺乏能有效识别手术获益人群的方法.因此,本研究分析局部手术与dnMBC患者预后的关系,并构建预后预测模型和探讨手术治疗的潜在获益人群. 方法:从SEER数据库中获取2010-2019年经病理诊断的dnMBC病例资料,根据乳房原发灶是否接受手术将患者划分为手术组和非手术组,采用倾向评分匹配法按1∶1匹配均衡两组间的基线特征,匹配后病例按7∶3随机分为训练集和验证集.采用多因素Cox比例风险模型分析乳腺癌特异生存(BCSS)独立预后因素并构建预测模型,使用C指数、时间依赖曲线下面积(AUC)、校准曲线及决策曲线分析(DCA)在训练集和验证集进行模型区分度、校准度和临床适用度验证.根据预测模型计算患者的预后风险评分,并将患者划分为低、中、高风险组,采用Kaplan-Meier法分析各组患者手术治疗与预后的关系. 结果:匹配后2 034例患者纳入分析,中位年龄56(48~63)岁,其中训练集1441例、验证集593例.中位随访27(11~48)个月,发生乳腺癌相关死亡963例(47.35%).手术患者和未手术患者估算的3 年 BCSS 率分别为 53.7%(95%CI=50.3%~57.3%)、63.1%(95%CI=59.9%~66.5%),生存分析显示,手术能明显提高dnMBC患者的BCSS(HR=0.72,95%CI=0.63~0.82,P<0.001).多因素Cox模型分析显示,种族、组织学分级、肿瘤T分期、脑转移、肺转移、骨转移、肝转移、激素受体状态、HER-2状态、局部手术及化疗是独立预后因素(均P<0.05).基于独立预后因素构建预后预测模型,在训练集和验证集分别进行模型验证.验证结果显示:C指数分别为0.707(95%CI=0.685~0.728)和0.705(95%CI=0.672~0.738);时间依赖的AUC值均在0.7~0.8之间;训练集和验证集的1、2、3年校准曲线显示预计生存率和实际生存率之间吻合度较高;DCA显示预测模型有较好的临床净获益.根据预测模型将患者划分为低、中、高风险组,生存分析显示,低风险组患者接受手术可改善BCSS(训练集:P<0.000 1;验证集:P=0.003 8),中、高风险组手术治疗未见改善预后. 结论:根据dnMBC患者的临床病理特征及诊疗资料构建的预测模型,能对患者预后进行分层并评估局部手术的潜在获益.该预测模型需在前瞻性研究中进一步验证和优化.

Background and Aims:The prognostic value of surgical intervention in patients with de novo metastatic breast cancer(dnMBC)has long been controversial.Some patients may benefit from local surgical treatment,but there is currently no effective method to identify those who would benefit from surgery.Therefore,this study was conducted to analyze the relationship between local surgery and prognosis in dnMBC patients,construct a prognostic prediction model,and determine the potential beneficiary group. Methods:Data of pathologically diagnosed dnMBC cases from 2010 to 2019 were obtained from the SEER database.Patients were divided into surgery and non-surgery groups based on whether they received surgery on the primary breast lesion.Propensity score matching(1∶1)was used to balance baseline characteristics between the two groups.The matched cases were randomly split into training and validation sets in a 7∶3 ratio.A multivariate Cox proportional hazards model was employed to analyze independent prognostic factors for breast cancer-specific survival(BCSS)and to construct a prediction model.The model's discrimination,calibration,and clinical utility were evaluated using the C-index,time-dependent area under the curve(AUC),calibration curves,and decision curve analysis(DCA)in both the training and validation sets.Prognostic risk scores were calculated based on the prediction model,and patients were categorized into low,intermediate,and high-risk groups.The relationship between surgical treatment and prognosis in each risk group was analyzed using the Kaplan-Meier method. Results:After matching,2 034 patients were included in the analysis,with a median age of 56(48-63)years.The training set comprised 1 441 cases,and the validation set comprised 593 cases.The median follow-up was 27(11-48)months,during which 963 breast cancer-related deaths(47.35%)occurred.The estimated 3-year BCSS rates for surgery and non-surgery patients were 53.7%(95%CI=50.3%-57.3%)and 63.1%(95%CI=59.9%-66.5%),respectively.Survival analysis showed that surgery significantly improved BCSS in dnMBC patients(HR=0.72,95%CI=0.63-0.82,P<0.001).Multivariate Cox model analysis indicated that race,histological grade,tumor T stage,brain metastasis,lung metastasis,bone metastasis,liver metastasis,hormone receptor status,HER-2 status,local surgery,and chemotherapy were independent prognostic factors(all P<0.05).A prognostic prediction model was constructed based on these independent prognostic factors,and the model was validated in both the training and validation sets.The validation results showed that the C-index was 0.707(95%CI=0.685-0.728)and 0.705(95%CI=0.672-0.738),respectively;the time-dependent AUC values were all between 0.7 and 0.8;the 1-,2-,and 3-year calibration curves in both sets indicated a high concordance between predicted and actual survival rates;DCA demonstrated a good clinical net benefit of the prediction model.According to the prediction model,patients were divided into low,medium,and high-risk groups.Survival analysis revealed that surgery improved BCSS in the low-risk group(training set:P<0.000 1;validation set:P=0.003 8),while no improvement in prognosis was observed in the medium and high-risk groups. Conclusion:The prognostic prediction model developed based on the clinicopathologic characteristics of dnMBC patients can stratify patients and assess the potential benefit of local surgery.This model requires further validation and optimization in prospective studies.

李芳芳;尹恒;杨宏伟;樊莉;牟德武;陈茂山

四川省遂宁市中心医院手术麻醉部,四川遂宁 629000四川省遂宁市中心医院乳腺甲状腺外科,四川遂宁 629000

临床医学

乳腺肿瘤外科手术列线图SEER规划

Breast NeoplasmsSurgical Procedures,OperativeNomogramsSEER Program

《中国普通外科杂志》 2024 (005)

719-731 / 13

超声医学工程国家重点实验室开放基金资助项目(2021KFKT015);四川省卫生健康委员会科技项目—临床研究专项基金资助项目(23LCYJ003);四川省遂宁市青年科技人才托举工程基金资助项目(遂科协发[2021]6号);吴阶平医学基金会临床科研专项基金资助项目(320.6750.2022-19-20).

10.7659/j.issn.1005-6947.2024.05.005

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