背景:男性乳腺癌(MBC)是一种罕见的乳腺癌亚型,预后因素研究有限。这项研究的目的是开发一种独特的列线图,用于预测MBC患者的总体生存率(OS)和乳腺癌特异性生存率(BCSS)。
方法:从2010年至2020年,监测男性乳腺癌患者的临床特征,流行病学和最终结果(SEER)数据库。在单变量和多变量分析之后,创建了OS和BCSS的列线图。进一步生成Kaplan-Meier图以说明独立风险变量与生存之间的关系。通过使用时间依赖性受试者工作特征曲线(AUC)和校准曲线下的面积来测量列线图的辨别能力。此外,当列线图用于指导临床实践时,我们还使用决策曲线分析(DCA)来评估临床有用性和净临床获益.
结果:本研究共纳入2143例患者。单因素和多因素分析表明,年龄,grade,手术,化疗状态,脑转移状态,子类型,婚姻状况,种族,和AJCC-T,AJCC-N,AJCC-M分期与OS显著相关。肺转移,年龄,婚姻状况,grade,手术,和AJCC-T,AJCC-N,AJCC-M分期与BCSS显著相关。通过包含这些变量,在SEER队列中构建了预测性列线图.然后,通过受试者工作特征(ROCs)曲线和校准图,可以在验证队列中很好地验证.此外,列线图显示了更好的决策曲线分析(DCA)结果,表明能够更准确地预测生存概率。
结论:我们创建并验证了一个独特的列线图,可以帮助临床医生识别高危MBC患者并预测其OS/BCSS。
BACKGROUND: Male breast cancer (MBC) represents a rare subtype of breast cancer, with limited prognostic factor studies available. The purpose of this research was to develop a unique nomogram for predicting MBC patient overall survival (OS) and breast cancer-specific survival (BCSS).
METHODS: From 2010 to 2020, clinical characteristics of male breast cancer patients were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Following univariate and multivariate analyses, nomograms for OS and BCSS were created. Kaplan-Meier plots were further generated to illustrate the relationship between independent risk variables and survival. The nomogram\'s ability to discriminate was measured by employing the area under a time-dependent receiver operating characteristic curve (AUC) and calibration curves. Additionally, when the nomogram was used to direct clinical practice, we also used decision curve analysis (DCA) to evaluate the clinical usefulness and net clinical benefits.
RESULTS: A total of 2143 patients were included in this research. Univariate and multivariate analysis showed that age, grade, surgery, chemotherapy status, brain metastasis status, subtype, marital status, race, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with OS. Lung metastasis, age, marital status, grade, surgery, and AJCC-T, AJCC-N, and AJCC-M stages were significantly correlated with BCSS. By comprising these variables, a predictive nomogram was constructed in the SEER cohort. Then, it could be validated well in the validation cohort by receiver operating characteristics (ROCs) curve and calibration plot. Furthermore, the nomogram demonstrated better decision curve analysis (DCA) results, indicating the ability to forecast survival probability with greater accuracy.
CONCLUSIONS: We created and validated a unique nomogram that can assist clinicians in identifying MBC patients at high risk and forecasting their OS/BCSS.