Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Jinping District, Shantou, 515041, Guangdong, China.
Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, Longhu People's Hospital, Shantou, 515041, China.
Clin Transl Oncol. 2024 Feb;26(2):389-397. doi: 10.1007/s12094-023-03316-0. Epub 2023 Sep 15.
To study the clinicopathological variables connected with disease-free survival (DFS) as well as overall survival (OS) in patients who are ER-positive or HER2-negative and to propose nomograms for predicting individual risk.
In this investigation, we examined 585 (development cohort) and 291 (external validation) ER-positive, HER2-negative breast cancer patients from January 2010 to January 2014. From January 2010 to December 2014, we retrospectively reviewed and analyzed 291 (external validation) and 585 (development cohort) HER2-negative, ER-positive breast cancer patients. Cox regression analysis, both multivariate and univariate, confirmed the independence indicators for OS and DFS.
Using cox regression analysis, both multivariate and univariate, the following variables were combined to predict the DFS of development cohort: pathological stage (HR = 1.391; 95% CI = 1.043-1.855; P value = 0.025), luminal parting (HR = 1.836; 95% CI = 1.142-2.952; P value = .012), and clinical stage (HR = 1.879; 95% CI = 1.102-3.203; P value = 0.021). Endocrine therapy (HR = 3.655; 95% CI = 1.084-12.324; P value = 0.037) and clinical stage (HR = 6.792; 95% CI = 1.672-28.345; P value = 0.009) were chosen as predictors of OS. Furthermore, we generated RS-OS and RS-DFS. According to the findings of Kaplan-Meier curves, patients who are classified as having a low risk have considerably longer DFS and OS durations than patients who are classified as having a high risk.
To generate nomograms that predicted DFS and OS, independent predictors of DFS in ER-positive/HER2-negative breast cancer patients were chosen. The nomograms successfully stratified patients into prognostic categories and worked well in both internal validation and external validation.
研究与 ER 阳性、HER2 阴性患者无病生存（DFS）和总生存（OS）相关的临床病理变量，并提出预测个体风险的列线图。
本研究纳入了 2010 年 1 月至 2014 年 1 月期间 585 例（研发队列）和 291 例（外部验证）ER 阳性、HER2 阴性乳腺癌患者。2010 年 1 月至 2014 年 12 月，我们回顾性分析了 291 例（外部验证）和 585 例（研发队列）HER2 阴性、ER 阳性乳腺癌患者。Cox 回归分析，包括多变量和单变量分析，证实了 OS 和 DFS 的独立预测指标。
采用 Cox 回归分析，包括多变量和单变量分析，将以下变量组合用于预测研发队列的 DFS：病理分期（HR=1.391；95%CI=1.043-1.855；P 值=0.025）、管腔分型（HR=1.836；95%CI=1.142-2.952；P 值=0.012）和临床分期（HR=1.879；95%CI=1.102-3.203；P 值=0.021）。内分泌治疗（HR=3.655；95%CI=1.084-12.324；P 值=0.037）和临床分期（HR=6.792；95%CI=1.672-28.345；P 值=0.009）被选为 OS 的预测指标。此外，我们生成了 RS-OS 和 RS-DFS。根据 Kaplan-Meier 曲线的结果，低危组患者的 DFS 和 OS 明显长于高危组患者。
选择 ER 阳性/HER2 阴性乳腺癌患者 DFS 的独立预测指标，生成预测 DFS 和 OS 的列线图。该列线图成功地将患者分为预后类别，在内部验证和外部验证中均表现良好。
