CT影像组学在预测非小细胞肺癌淋巴结转移中的价值

目的探讨基于胸部CT影像组学在术前预测非小细胞肺癌淋巴结转移中的价值。方法回顾性分析经术后病理证实的143例非小细胞肺癌病人临床、胸部CT增强影像资料。按照7:3比例，随机分为训练组（n=100）和验证组（n=43）。在静脉期图像上提取肿瘤的影像组学特征，采用最小绝对收缩选择算子（LASSO）逻辑回归用于数据降维、特征筛选。分别基于影像组学特征和临床-影像特征（最大径、毛刺征）构建预测模型。采用受试者工作特征曲线（ROC）的曲线下面积（AUC）评价不同模型的鉴别预测效能，并对模型的ROC曲线行Delong检验；在验证组中评估其预测效能。结果共提取939个影像组学特征，经筛选最终得到6个最优特征并建立影像组学预测模型。对于术前预测淋巴结转移的效能，在训练组中，影像组学模型AUC为0.864（95%CI:0.781~0.924），大于临床模型的AUC为0.662（95%CI:0.561~0.754）（P < 0.01）；在验证组中，影像组学模型AUC为0.860（95%CI:0.720~0.964），大于临床模型的AUC为0.664（95%CI:0.504~0.880）（P < 0.05）。结论基于胸部CT增强图像提取影像组学特征及其构建的预测模型，影像组学模型的效能高于临床模型，可以作为一种预测非小细胞癌病人淋巴结是否转移的辅助工具，具有良好的临床应用前景。

The value of CT radiomics in the prediction of lymph node metastasis in non-small cell lung cancer

ObjectiveTo explore the value of chest enhanced CT radiomics in the prediction of lymph node metastasis in patients with non-small cell lung cancer(NSCLC).MethodsThe clinical and chest enhanced CT data of 143 NSCLC patients confirmed by pathologically were retrospectively analyzed.The patients were randomly divided into the training group(n=100) and verification group(n=43) according to the ratio of 7:3.The venous phase images were used to extract the radiomics features.The least absolute shrinkage and selection operator(LASSO) logistic regression was used for data dimension reduction and feature selection.Two predictive models were constructed using the radiomics features and clinical-imaging characteristics(the maximum meridian and burr sign).The AUCs of ROC was used to evaluate the predictive effectiveness of model.The ROC curve of model was tested by Delong test.The predictive efficacy was evaluated in validation group.ResultsA total of 939 radiomics features were extracted, 6 optimal features were finally selected, and the prediction model was established.In the training group, the AUC of the radiomics model was 0.864(95%CI: 0.781~0.924), which was higher than that of the clinical model0.662 (95%CI: 0.561~0.754)](P < 0.01).In the validation group, the AUC of the radiomics model was 0.860(95%CI: 0.720~0.964), which was greater than that of the clinical model(0.664 (95%CI: 0.504~0.880)](P < 0.05).ConclusionsBased on the image omics features extracted from the chest CT enhanced images and constructing the prediction model, the efficacy of the radiomics model was higher than that of the clinical model.The CT radiomics can be used as an auxiliary tool to predict lymph node metastasis in patients with non-small cell cancer, which has a good clinical application prospect.