Naples Prognostic Score (NPS) as a Novel Prognostic Score for Stage III Breast Cancer Patients: A Real-World Retrospective Study

那不勒斯预后评分(NPS)作为一种新型的III期乳腺癌患者预后评分:一项真实世界回顾性研究

阅读:2

Abstract

OBJECTIVE: This study aims to explore whether Naples prognostic score (NPS) serves as a novel and original prognostic tool for predicting long-term survival in stage III breast cancer patients undergoing operation. METHODS: This retrospective study included 306 cases of stage III breast cancer patients hospitalized in our hospital from January 2014 to December 2018. In this study, NPS was based on five objective markers: (1) serum albumin level; (2) total cholesterol; (3) neutrophil to lymphocyte ratio; (4) lymphocyte to monocyte ratio. Survival curves of DFS and OS differences were visualized by Kaplan-Meier method and Log rank test. The variables with p < 0.05 in univariate analysis were performed in the multivariate Cox proportional hazard model analysis, and the p-values < 0.05 was considered the underlying independent variables. Nomogram was constructed by the multivariate Cox proportional hazard model analysis. RESULTS: Significant variations for DFS and OS categorized according to prognostic risk for the different NPS (DFS: χ(2)=24.926, P < 0.0001; OS: χ(2)=31.207, P < 0.0001). According to multivariable Cox analysis, NPS was an independent prognostic factor of DFS [Group 0 had significantly better prognosis than group 1 (HR = 2.733, 95% CI: 1.446-5.166, P = 0.002) and group 2 (HR = 4.990, 95% CI: 2.555-9.746), P < 0.001)] and OS [Group 0 had significantly better prognosis than group 1 (HR = 2.437, 95% CI: 1.288-4.610, P = 0.006) and group 2 (HR = 5.707, 95% CI: 2.900-11.231), P < 0.001)], respectively. Nomogram prognostic model exhibited excellent predictive performance on DFS [C-index: 0.692 (95% CI: 0.584-0.782)] and OS [C-index: 0.711 (95% CI: 0.606-0.797)] for stage III breast cancer. CONCLUSION: NPS serves as a predictive tool for assessing the prognosis of stage III breast cancer after surgery. Nomogram prognostic model based on NPS show good prediction ability.

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。