Predicting Breast Cancer Recurrence Following Breast-Conserving Therapy: A Single-Institution Analysis Consisting of 764 Chinese Breast Cancer Cases
Predicting Breast Cancer Recurrence Following Breast-Conserving Therapy: A Single-Institution Analysis Consisting of 764 Chinese Breast Cancer Cases
复制标题
预测保乳治疗后乳腺癌复发：764 例中国乳腺癌病例的单机构分析
DOI：
10.1245/s10434-011-1626-2
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发表时间：
2011-09-01
影响因子：
3.7
通讯作者：
Shao, Zhi-Ming
中科院分区：
文献类型：
--
作者：
Li, Shuang;Yu, Ke-Da;Shao, Zhi-Ming
背景本研究的目的是确定与保乳手术（BCS）治疗的女性复发相关的预后因素，并通过构建预测模型来预测保乳治疗（BCT）后的复发。方法回顾性分析1995年至2008年在上海肿瘤中心连续接受BCT治疗的764例浸润性乳腺癌患者，通过单因素和多因素分析确定局部复发的独立危险因素。 （LRR）和所有复发事件。采用Logistic回归构建复发预测模型，并通过受试者工作特征（ROC）曲线进一步评估。结果5年局部无复发生存率（LRRFS）和无复发生存率（RFS）分别为90.8%和88.4%。多变量分析揭示了 LRRFS 的 1 个独立预测因素（淋巴结，P= .0049）和 RFS 的 3 个独立预测因素（淋巴结，P= .0036；分子亚型，P= .0021；组织学分级，P= .041）。这三个变量进入逻辑回归以建立循环预测模型。 ROC曲线显示，所建立模型的曲线下面积（AUC）为0.70（95%置信区间：0.61-0.78）。该模型可以将患者分为“高风险复发”和“低风险复发”组，并可以成功预测其预后（P < .00001）。结论淋巴结状态、分子亚型和分级信息可以帮助医生评估接受 BCT 治疗的女性的复发风险。我们的新模型可能有助于中国患者 BCT 后复发预测的临床实践，但还需要进一步的验证研究。
BackgroundThe purpose of this study was to identify prognostic factors related to recurrence in women treated with breast-conserving surgery (BCS) and to predict the recurrence following breast-conserving therapy (BCT) by constructing a prediction model.MethodsThe retrospective analysis included 764 consecutive invasive breast cancer patients treated with BCT in Shanghai Cancer Center between 1995 and 2008. Univariate and multivariate analysis were performed to identify independent risk factors for locoregional recurrence (LRR) and all the recurrence events. Logistic regression was used to construct a recurrence prediction model, which was further evaluated by receiver operating characteristics (ROC) curves.ResultsThe 5-year locoregional recurrence-free survival (LRRFS) and recurrence-free survival (RFS) rates were 90.8 and 88.4%, respectively. Multivariate analysis revealed 1 independent predictive factor for LRRFS (lymph node,P= .0049) and three independent predictive factors for RFS (lymph node,P= .0036; molecular subtype,P= .0021; histological grade,P= .041). These three variables entered into logistic regression to establish a recurrent prediction model. ROC curve showed that the area under the curve (AUC) of the established model was 0.70 (95% confidence interval: 0.61–0.78). This model could classify patients into “high-risk recurrence” and “low-risk recurrence” groups and could successfully predict their prognosis (P< .00001).ConclusionsThe information of lymph node status, molecular subtype, and grade may help doctors to evaluate recurrence risk of a woman treated with BCT. Our new model might be helpful in clinical practice for recurrence prediction after BCT in Chinese patients, though further validation studies are needed.