OBJECTIVES To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients. METHODS This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models. RESULTS The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both<0.001). CONCLUSION A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients.

中文翻译：

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基于CT的IA期非小细胞肺癌术前淋巴结转移的预测放射学模型的开发。

目的开发和验证基于临床参数，放射学特征以及两者结合的基于CT的IA期非小细胞肺癌（NSCLC）患者的淋巴结转移（LNM）的预测模型。方法这项回顾性研究纳入了来自我院的649例基于CT的术前IA期NSCLC患者。649例患者中的一百三十八名（21％）在手术后患有LNM。从静脉相衬增强计算机断层扫描（CECT）中提取了总共396个放射学特征。训练组包括455位患者（97位有LNM和358位没有LNM），测试组包括194位患者（41位有LNM和153位没有LNM）。最小绝对收缩和选择算子（LASSO）算法用于放射特征选择。随机森林（RF）用于模型开发。开发了三种模型（临床模型，放射学模型和组合模型）来预测NSCLC早期患者的LNM。使用这三个模型，使用接收器工作特性（ROC）曲线（AUC）值和决策曲线分析下的面积来评估LNM状态（有或没有LNM）下的性能。结果ROC分析（也是决策曲线分析）显示了放射性组模型（训练和测试的AUC值分别为0.898和0.851）和组合模型的LNM的预测性能（分别为0.911和0.860）。两者的表现均优于临床模型（分别为0.739和0.614；延长检验的p值均<0.001）。结论使用CE-CT静脉期的放射组学模型可预测基于CT的IA期非小细胞肺癌患者的LNM。