Prognostic factors for patients with metastatic breast cancer: a literature review

转移性乳腺癌患者的预后因素:文献综述

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Abstract

BACKGROUND: To treat metastatic breast cancer (MBC) more precisely, many efforts have been made to identify prognostic factors of MBC in many studies. This review aims to qualitatively summarize these studies and to provide a reference for the research of MBC. METHODS: Relevant papers were searched on PubMed, with the search terms including MBC, prognostic factors and prognosis, and the studies aimed at exploring prognostic factors for patients with histologically confirmed MBC, including stage IV at initial diagnosis and metastatic recurrence, were included. RESULTS: A total of 30 papers were included at last. An analysis of prognostic factors frome those studies was conducted. Age at primary diagnosis (6 studies), performance status (4 studies), histological grade (4 studies), hormonal receptor (HR) status (9 studies) and site of metastasis (12 studies) were universally acknowledged prognostic factors. There were four studies revealing that short DFS was significantly associated with better OS, while there was one study not revealing this association. There were various results in different studies with a reference to efficacy. Surgery and endocrine therapy were related to a better prognosis (3 studies). Targeted therapies could also conduce to the prognosis. However, there was still a contention on the role of radiotherapy. In particular, a model was brought out to calculate the risk of death in MBC. Meanwhile, it was found that some biomarkers are related to prognosis as well as per the latest findings in some studies. CONCLUSIONS: In summary, intrinsic characteristics of tumors such as HR status and histological grade are the main factors affecting the prognosis of patients with MBC. Besides traditional factors, some new drugs and biomarkers are also associated with the prognosis of patients with MBC. In the future, the focus of studies shall be on the construction of a practical and high-quality model to predict the risk of death in MBC patients.

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