A nomogram and risk stratification system for predicting survival in T1-2N0-1 breast cancer patients with liver metastasis in females: a population-based study

一项基于人群的研究:用于预测女性T1-2N0-1期乳腺癌肝转移患者生存率的列线图和风险分层系统

阅读:1

Abstract

PURPOSE: Liver was one of the most common distant metastatic sites in breast cancer. Patients with distant metastasis were identified as American Joint Committee on Cancer (AJCC) stage IV indicating poor prognosis. However, few studies have predicted the survival in females with T1-2N0-1 breast cancer who developed liver metastasis. This study aimed to explore the clinical features of these patients and establish a nomogram to predict their overall survival. RESULTS: 1923 patients were randomly divided into training (n = 1154) and validation (n = 769) cohorts. Univariate and multivariate analysis showed that age, marital status, race, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), chemotherapy, surgery and bone metastasis, brain metastasis were considered the independent prognostic indicators. We developed a nomogram according to these ten parameters. The consistency index (c-index) was 0.72 (95% confidence interval CI 0.70-0.74) in the training cohort, 0.72 (95% CI 0.69-0.74) in the validation cohort. Calibration plots indicated that the nomogram-predicted survival was consistent with the recorded 1-, 3- and 5-year prognoses. Decision curve analysis curves in both the training and validation cohorts demonstrated that the nomogram showed better prediction than the AJCC TNM (8th) staging system. Kaplan Meier curve based on the risk stratification system showed that the low-risk group had a better prognosis than the high-risk group (P < 0.001). CONCLUSIONS: A predictive nomogram and risk stratification system were constructed to assess prognosis in T1-2N0-1 breast cancer patients with liver metastasis in females. The risk model established in this study had good predictive performance and could provide personalized clinical decision-making for future clinical work.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。