Dehing-Oberije Cary, Yu Shipeng, De Ruysscher Dirk, Meersschout Sabine, Van Beek Karen, Lievens Yolande, Van Meerbeeck Jan, De Neve Wilfried, Rao Bharat, van der Weide Hiska, Lambin Philippe
Department of Radiotherapy, MAASTRO Clinic, Research Institute of Growth and Development, University Hospital Maastricht, University Maastricht, The Netherlands.
Int J Radiat Oncol Biol Phys. 2009 Jun 1;74(2):355-62. doi: 10.1016/j.ijrobp.2008.08.052. Epub 2008 Dec 25.
Radiotherapy, combined with chemotherapy, is the treatment of choice for a large group of non-small-cell lung cancer (NSCLC) patients. Recent developments in the treatment of these patients have led to improved survival. However, the clinical TNM stage is highly inaccurate for the prediction of survival, and alternatives are lacking. The objective of this study was to develop and validate a prediction model for survival of NSCLC patients, treated with chemoradiotherapy.
The clinical data from 377 consecutive inoperable NSCLC patients, Stage I-IIIB, treated radically with chemoradiotherapy were collected. A prognostic model for 2-year survival was developed, using 2-norm support vector machines. The performance of the model was expressed as the area under the curve of the receiver operating characteristic and assessed using leave-one-out cross-validation, as well as two external data sets.
The final multivariate model consisted of gender, World Health Organization performance status, forced expiratory volume in 1 s, number of positive lymph node stations, and gross tumor volume. The area under the curve, assessed by leave-one-out cross-validation, was 0.74, and application of the model to the external data sets yielded an area under the curve of 0.75 and 0.76. A high- and low-risk group could be clearly identified using a risk score based on the model.
The multivariate model performed very well and was able to accurately predict the 2-year survival of NSCLC patients treated with chemoradiotherapy. The model could support clinicians in the treatment decision-making process.
放疗联合化疗是一大类非小细胞肺癌（NSCLC）患者的首选治疗方法。这些患者治疗方面的最新进展已使生存率提高。然而，临床TNM分期在预测生存率方面准确性很差，且缺乏替代方法。本研究的目的是开发并验证一个用于接受放化疗的NSCLC患者生存率的预测模型。
收集了377例连续的I-IIIB期无法手术的NSCLC患者接受根治性放化疗的临床数据。使用2-范数支持向量机开发了一个2年生存率的预后模型。该模型的性能以受试者工作特征曲线下面积表示，并采用留一法交叉验证以及两个外部数据集进行评估。
最终的多变量模型包括性别、世界卫生组织体能状态、第1秒用力呼气量、阳性淋巴结站数和肿瘤总体积。通过留一法交叉验证评估的曲线下面积为0.74，将该模型应用于外部数据集得到的曲线下面积分别为0.75和0.76。基于该模型的风险评分能够清晰地识别出高风险组和低风险组。
该多变量模型表现良好，能够准确预测接受放化疗的NSCLC患者的2年生存率。该模型可为临床医生的治疗决策过程提供支持。
