A nomogram based on inflammation and nutritional biomarkers for predicting the survival of breast cancer patients

作者：Caibiao Wei, Huaying Ai, Dan Mo, Peidong Wang, Liling Wei, Zhimin Liu, Peizhang Li, Taijun Huang, Miaofeng Liu · 发表于：Frontiers in Endocrinology · 年份：2024 · DOI：10.3389/fendo.2024.1388861 · 被引用次数：8 · 研究领域：Inflammatory Biomarkers in Disease Prognosis、Ferroptosis and cancer prognosis、Cancer Risks and Factors

Background: We aim to develop a new prognostic model that incorporates inflammation, nutritional parameters and clinical-pathological features to predict overall survival (OS) and disease free survival (DFS) of breast cancer (BC) patients. Methods: The study included clinicopathological and follow-up data from a total of 2857 BC patients between 2013 and 2021. Data were randomly divided into two cohorts: training (n=2001) and validation (n=856) cohorts. A nomogram was established based on the results of a multivariate Cox regression analysis from the training cohorts. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. Furthermore, decision curve analysis (DCA) was performed to assess the clinical value of the nomogram. Results: A nomogram was developed for BC, incorporating lymphocyte, platelet count, hemoglobin levels, albumin-to-globulin ratio, prealbumin level and other key variables: subtype and TNM staging. In the prediction of OS and DFS, the concordance index (C-index) of the nomogram is statistically greater than the C-index values obtained using TNM staging alone. Moreover, the time-dependent AUC, exceeding the threshold of 0.7, demonstrated the nomogram's satisfactory discriminative performance over different periods. DCA revealed that the nomogram offered a greater overall net benefit than the TNM staging system. Conclusion: The nomogram incorporating inflammation, nutritional ...