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摘要: 在非小细胞肺癌的临床诊疗中，淋巴结是否转移对于医生制定手术方案有重要指导意义.但是目前临床上缺乏能够安全准确地预测非小细胞肺癌淋巴结转移的方法.本文应用影像组学方法对肺部CT影像进行定量分析来实现对非小细胞肺癌淋巴结是否转移的预测.从广东省人民医院收集了564例满足分析要求的非小细胞肺癌病例数据，并从每例CT影像中提取了386个定量影像特征，包括肿瘤的三维形状特征，表面纹理特征，Gabor特征以及小波特征：然后利用Lasso logistic regression（LLR）来构造非小细胞肺癌淋巴结转移的影像组学标签（Rad-score），并结合临床信息进行多元分析，构造了诺模图个性化预测模型.其中，LLR淋巴结转移预测模型性能在训练集上AUC为0.710，测试集AUC为0.712：在个性化诺模图上，用所有数据进行预测，得到C-index为0.724（95% CI：0.678~0.770），在一致性曲线上表现较佳，可为临床决策提供有价值的信息.Abstract: In the clinical diagnosis and treatment of non-small cell lung cancer, it makes great sense for guiding doctors to make operation plans according to lymph node metastasis status. However, a current weak point in clinical is the lack of a method that can be used to predict lymph node metastasis for non-small cell lung cancer safely and accurately. In this article, radiomic method was applied to the lung CT images to achieve quantitative analysis for the prediction of lymph node metastasis for non-small cell lung cancer. We collected 564 non-small cell lung cancer cases that could satisfy the data recruitment from Guangdong General Hospital, from which 386 quantitative radiomic features were extracted each, including the tumor's three-dimensional shape features, texture features, Gabor features and wavelet features. Then, Lasso logistic regression (LLR) was used to construct the radiomic signature (Rad-score) for the lymph node metastasis of NSCLC. With multivariate analysis of clinical information, the customized prediction nomogram model was built. The performance of the LLR model was shown to have an AUC of 0.710 in the training set and 0.712 in the validation set. Our nomogram model had a C-index of 0.724 (95% CI:0.678~0.770) and performed well on the consistency, providing valuable information for clinical decision-making.
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Key words:
- Radiomics /
- lymph node metastasis /
- Lasso logistic regression /
- nomogram /
- calibration curve
1) 本文责任编委 朱朝 -
图 4 $\lambda $与变量数目对应走势
Fig. 4 The trend of the parameters and the number of variables
表 1 训练集和测试集病人的基本情况
Table 1 Basic information of patients in the training set and test set
基本项 训练集($N=400$) $P$值 测试集($N=164$) $P$值 性别 男 144 (36 %) 0.896 78 (47.6 %) 0.585 女 256 (64 %) 86 (52.4 %) 吸烟 是 126 (31.5 %) 0.030* 45 (27.4 %) 0.081 否 274 (68.5 %) 119 (72.6 %) EGFR 缺失 36 (9 %) 4 (2.4 %) 突变 138 (34.5 %) $ < $0.001* 67 (40.9 %) 0.112 正常 226 (56.5 %) 93 (56.7 %) 
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表 2 Lasso选择得到的参数
Table 2 Parameters selected by Lasso
Lasso选择的参数 含义 数值 $P$值 $V0$ 截距项 2.079115 $V179$ 横向小波分解90度共生矩阵Contrast特征(Contrast_2_90) 0.0000087 < 0.001*** $V230$ 横向小波分解90度共生矩阵Entropy特征(Entropy_3_180) $-$3.573315 < 0.001*** $V591$ 表面积与体积的比例(Surface to volume ratio) $-$1.411426 < 0.001***
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表 3 不同方法对比结果
Table 3 Comparison results of different methods
方法 训练集(AUC) 测试集(AUC) 召回率 LLR 0.710 0.712 0.75 SVM 0.698 0.654 0.75 NB 0.718 0.681 0.74
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