Zhang Fanrong, Zheng Weihui, Ying Lisha, Wu Junzhou, Wu Shaoyuan, Ma Shenglin, Su Dan
Cancer Research Institute, Zhejiang Cancer Hospital and Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology of Zhejiang Province, Hangzhou, China.
School of Life Sciences, Jiangsu Normal University, Xuzhou, China.
Ann Surg Oncol. 2016 Sep;23(9):3033-9. doi: 10.1245/s10434-016-5206-3. Epub 2016 Apr 18.
Brain metastasis is a major cause leading to the failure of treatment management for non-small cell lung cancer (NSCLC) patients. The goal of this study was to establish an effective nomogram for prediction of brain metastases of resected NSCLC patients.
We retrospectively investigated 637 operable NSCLC patients who received treatment at Zhejiang Cancer Hospital, China. A Cox proportional hazards regression model was performed to identify significant risk factors, and a nomogram was developed for predicting 3- and 5-year brain metastases rates.
Multivariate analysis identified four independent risk factors: neuron-specific enolase, histological type, number of metastatic lymph nodes, and tumor grade, and a nomogram was developed based on these factors. The effectiveness of the nomogram was validated using an internal bootstrap resampling approach, showing that the nomogram exhibited a sufficient level of discrimination according to the C-index (0.74, 95 % confidence interval 0.67-0.82).
The nomogram developed in this study demonstrated its discrimination capability for predicting 3- and 5-year occurrence of brain metastases, and can be used to identify high-risk patients.
脑转移是导致非小细胞肺癌（NSCLC）患者治疗失败的主要原因。本研究的目的是建立一个有效的列线图，用于预测接受手术切除的NSCLC患者发生脑转移的情况。
我们回顾性调查了在中国浙江省肿瘤医院接受治疗的637例可手术切除的NSCLC患者。采用Cox比例风险回归模型确定显著的危险因素，并建立一个列线图来预测3年和5年脑转移率。
多因素分析确定了四个独立的危险因素：神经元特异性烯醇化酶、组织学类型、转移淋巴结数量和肿瘤分级，并基于这些因素建立了列线图。采用内部自抽样重采样方法验证了列线图的有效性，结果显示，根据C指数（0.74，95%置信区间0.67 - 0.82），列线图具有足够的区分度。
本研究建立的列线图显示了其预测3年和5年脑转移发生情况的区分能力，可用于识别高危患者。
