一种预测乳腺癌患者复发的评分系统。

A scoring system to predict recurrence in breast cancer patients.

作者信息

Paredes-Aracil Esther, Palazón-Bru Antonio, Folgado-de la Rosa David Manuel, Ots-Gutiérrez José Ramón, Llorca-Ferrándiz Cristina, Alonso-Hernández Sonia, Coloma-Lidón José Vicente, Gil-Guillén Vicente Francisco

机构信息

General Surgery Service, General University Hospital of Elda, Ctra Sax-La Torreta S/N, Elda, 03600, Alicante, Spain.

Department of Clinical Medicine, Miguel Hernández University, Ctra Valencia-Alicante S/N, San Juan de Alicante, Alicante, 03550, Spain.

出版信息

Surg Oncol. 2018 Dec;27(4):681-687. doi: 10.1016/j.suronc.2018.09.005. Epub 2018 Sep 18.

DOI:10.1016/j.suronc.2018.09.005

PMID:30449493

Abstract

OBJECTIVE

Current breast cancer recurrence prediction models have limitations for clinical practice (statistical methodology, simplicity and specific populations). We therefore developed a new model that overcomes these limitations.

METHODS

This cohort study comprised 272 patients with breast cancer followed between 2003 and 2016. The main variable was time-to-recurrence (locoregional and/or metastasis) and secondary variables were its risk factors: age, postmenopause, grade, oestrogen receptor, progesterone receptor, c-erbB2 status, stage, multicentricity, diagnosis and treatment. A Cox model to predict recurrence was estimated with the secondary variables, and this was adapted to a points system to predict risk at 5 and 10 years from diagnosis. The model was validated internally by bootstrapping, calculating the C statistic and smooth calibration (splines). The system was integrated into a mobile application for Android.

RESULTS

Of the 272 patients with breast cancer, 47 (17.3%) developed recurrence in a mean time of 8.6 ± 3.5 years. The system variables were: age, grade, multicentricity and stage. Validation by bootstrapping showed good discrimination and calibration.

CONCLUSIONS

A points system has been developed to predict breast cancer recurrence at 5 and 10 years.

摘要

目的

当前的乳腺癌复发预测模型在临床实践中存在局限性（统计方法、简易性及特定人群方面）。因此，我们开发了一种新模型以克服这些局限性。

方法

这项队列研究纳入了2003年至2016年间随访的272例乳腺癌患者。主要变量为复发时间（局部区域复发和/或转移），次要变量为其危险因素：年龄、绝经状态、分级、雌激素受体、孕激素受体、c-erbB2状态、分期、多中心性、诊断及治疗情况。用次要变量估计预测复发的Cox模型，并将其调整为一个积分系统以预测诊断后5年和10年的风险。该模型通过自抽样法进行内部验证，计算C统计量和平滑校准（样条）。该系统被集成到一款安卓移动应用程序中。