IEEE J Biomed Health Inform. 2016 Jan;20(1):424-31. doi: 10.1109/JBHI.2014.2377517. Epub 2014 Dec 4.
Non-small cell lung cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Nevertheless, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. To address this problem, we used frequent pattern mining to establish the relationships of patient characteristics and tumor response in advanced NSCLC. Univariate analysis determined that smoking status, histology, epidermal growth factor receptor (EGFR) mutation, and targeted drug were significantly associated with response to targeted therapy. We applied four classifiers to predict treatment outcome from EGFR tyrosine kinase inhibitors. Overall, the highest classification accuracy was 76.56% and the area under the curve was 0.76. The decision tree used a combination of EGFR mutations, histology, and smoking status to predict tumor response and the output was both easily understandable and in keeping with current knowledge. Our findings suggest that support vector machines and decision trees are a promising approach for clinical decision support in the patient selection for targeted therapy in advanced NSCLC.
非小细胞肺癌（NSCLC）是最常见的肺癌类型，通常在晚期诊断。临床研究表明，分子靶向治疗可提高患者的生存率并改善生活质量。然而，实现 NSCLC 的个性化治疗面临许多挑战，包括整合临床和遗传数据以及缺乏临床决策支持工具来协助医生进行患者选择。为了解决这个问题，我们使用频繁模式挖掘来建立晚期 NSCLC 患者特征与肿瘤反应之间的关系。单因素分析确定吸烟状况、组织学、表皮生长因子受体（EGFR）突变和靶向药物与靶向治疗反应显著相关。我们应用了四种分类器来预测 EGFR 酪氨酸激酶抑制剂的治疗效果。总体而言，最高的分类准确性为 76.56%，曲线下面积为 0.76。决策树使用 EGFR 突变、组织学和吸烟状况的组合来预测肿瘤反应，输出结果既易于理解，又符合当前知识。我们的研究结果表明，支持向量机和决策树是为晚期 NSCLC 靶向治疗患者选择提供临床决策支持的一种很有前途的方法。
