The Value of Radiomics Based on Spectral CT Iodine Map for Diagnosing Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma
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摘要:
目的：探讨基于能谱CT碘图的影像组学特征对甲状腺乳头状癌患者颈部转移性淋巴结的诊断价值。方法：收集术前两周行颈部能谱CT检查的甲状腺乳头状癌患者，共纳入117枚转移性和176枚非转移性淋巴结，按照3︰1的比例随机分为训练集和验证集。从静脉期碘图中提取并筛选淋巴结的影像组学特征。采用Logistic回归分别建立影像组学模型、常规CT图像特征模型及联合模型，并绘制列线图将联合模型可视化。各模型的诊断效能、校准能力及临床实用性分别通过ROC曲线、校准曲线及决策曲线分析评估。结果：联合模型在训练集和验证集中均表现出最佳的诊断效能，其次是影像组学模型，且两者显著优于常规CT图像特征模型。所有模型均显示出良好的校准能力，决策曲线分析表明列线图的临床实用性优于其余两种模型。结论：能谱CT的影像组学特征在诊断甲状腺乳头状癌淋巴结转移方面表现出良好的性能，联合常规CT图像特征后诊断效能进一步提高。
Abstract:Objective: To investigate the value of radiomics features based on spectral CT iodine map for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid carcinoma. Methods: Seventy-eight patients with papillary thyroid carcinoma who underwent cervical energy spectrum CT within two weeks before surgery were retrospectively analyzed. We included 117 metastatic, 176 non-metastatic lymph nodes, which were then randomly divided into a training set and a validation set in a 3:1 ratio. Radiomics features were extracted and screened from venous phase iodine maps. Logistic regression model was used to construct diagnostic models based on CT image features, radiomics signature, and a combination of the two, respectively; a nomogram was then drawn to visualize the combined model. The diagnostic performance, calibration ability and clinical practicability of each model were evaluated by ROC curve, calibration curve and decision curve analysis, respectively. Results: The combined model showed optimal diagnostic performance in both the training and validation sets, followed by radiomics model. These two models outperformed the CT image features model in both the training and validation sets. All models showed good calibration, and decision curve analysis demonstrated the superiority of the nomogram over the other two models in terms of clinical usefulness. Conclusion: The radiomics signature of spectral CT showed good performance in diagnosing lymph node metastasis of papillary thyroid carcinoma. The diagnostic performance was further improved when combined with CT image features, which can be a useful tool to assist in clinical decision-making.
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Keywords:
- spectral CT /
- radiomics /
- thyroid neoplasms /
- papillary carcinoma /
- lymphatic metastasis
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图 2 甲状腺乳头状癌患者伴颈部转移性淋巴结（红色箭头）
Figure 2. A papillary thyroid cancer patient with metastatic cervical lymph nodes (red arrow)
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图 3 联合常规CT图像特征（形状、强化程度、强化方式及钙化）和Radscore绘制的列线图，用于判断转移性淋巴结的风险
Figure 3. Nomogram combined with conventional CT image features (shape, degree of enhancement, enhancement pattern, and calcification) and Radscore was used to determine the risk of metastatic lymph nodes
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图 4 CT图像特征模型、影像组学模型、列线图诊断甲状腺乳头状癌颈部转移淋巴结的ROC曲线
注：（a）为训练集，（b）为验证集。可见列线图的曲线下面积大于另外两种模型。
Figure 4. ROC curve of the CT image feature model, radiomics model and nomogram in diagnosing cervical lymph node metastasis of papillary thyroid carcinoma
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图 5 训练集（a）～（c）和验证集（d）～（f）中CT图像特征模型、影像组学模型和列线图的校准曲线，曲线越接近对角灰色虚线表示模型的预测能力越好
Figure 5. The calibration curves of the CT image feature model, radiomics model, and nomogram in the training set ((a)~(c)) and validation set ((d)~(f)). The closer the curve is to the diagonal gray dotted line, the better the predictive ability of the model
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图 6 CT图像特征模型、影像组学模型和列线图的决策曲线
注：曲线显示预测淋巴结转移的阈值概率在19％～88％ 时，列线图比CT图像特征模型和影像组学模型获得更多的净收益。
Figure 6. Decision curve analysis of the CT image feature model, radiomics model and nomogram
表 1 甲状腺乳头状癌患者颈部转移组与非转移组淋巴结的CT图像特征比较
Table 1 Comparison of CT image features of neck lymph nodes between metastatic and non-metastatic groups of patients with papillary thyroid carcinoma
组别 数量/枚 最大短径d/mm 形状 边缘 强化程度 5≤d<10 10≤d<35 规则 不规则 清楚 模糊 轻中度 明显 转移组 117 85 32 69 48 80 37 41 76 非转移组 176 134 42 144 32 142 34 115 61 统计检验 $\chi^2 $ 0.453 18.477 5.797 25.915 P 0.501 <0.001 <0.050 <0.001 组别 数量/枚 强化方式 钙化 囊变/坏死 结外侵犯 均匀 不均匀 有 无 有 无 有 无 转移组 117 54 63 14 103 16 101 8 109 非转移组 176 139 37 3 173 1 175 0 176 统计检验 $\chi^2 $ 33.680 13.540 22.091 Fisher P <0.001 <0.001 <0.001 <0.001 注：本研究中所测得淋巴结最大短径范围为5～35 mm。 
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表 2 三种模型诊断甲状腺乳头状癌颈部淋巴结转移纳入的logistic回归因素
Table 2 Logistic regression factors included in the diagnosis of cervical lymph node metastasis of papillary thyroid carcinoma by three models
模型 变量 P 回归系数 标准差 Wald值 OR值 95％ CI CT图像特征模型 形状 0.013 0.746 0.302 6.113 2.108 1.167～3.806 强化程度 0.000 1.007 0.270 13.938 2.737 1.613～4.643 强化方式 <0.001 1.148 0.280 16.834 3.153 1.821～5.456 钙化 0.035 1.480 0.700 4.469 4.394 1.113～17.335 影像组学模型 firstorder_90 Percentile
_wavelet_HLH0.000 -0.994 0.273 13.281 0.370 0.216～0.631 glcm_Correlation_wavelet_LHL <0.001 -1.143 0.280 16.695 0.319 0.184～0.551 glcm_Idm_wavelet_HLH 0.518 -0.155 0.239 0.418 0.857 0.535～1.369 glcm_Imc1_wavelet_LHL 0.036 -0.638 0.305 4.388 0.528 0.290～0.959 gldm_DependenceVariance
_log_sigma_3_0_mm_3D0.094 0.335 0.200 2.812 1.398 0.945～2.068 gldm_Dependence
Variance_wavelet_HHH0.055 0.404 0.211 3.673 1.497 0.990～2.262 glrlm_GrayLevelNon
UniformityNormalized_square0.118 -0.623 0.399 2.441 0.537 0.245～1.171 glrlm_LowGrayLevel
RunEmphasis_square0.689 -0.146 0.364 0.161 0.864 0.423～1.763 shape_Elongation_original 0.005 0.525 0.187 7.889 1.690 1.171～2.436 联合模型 形状 0.000 1.408 0.384 13.465 4.086 1.926～8.666 强化程度 0.012 0.817 0.326 6.303 2.265 1.196～4.286 强化方式 0.016 0.820 0.341 5.788 2.270 1.164～4.426 钙化 0.061 1.434 0.765 3.509 4.195 0.935～18.805 Radscore <0.001 5.486 0.731 56.269 241.193 57.528～1011.227
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表 3 训练集和验证集中CT图像特征模型、影像组学模型和列线图的诊断效能比较
Table 3 Comparison of diagnostic performance of the CT image feature model, radiomics model, and nomogram in training and validation sets
数据集 模型 AUC 95％CI P 灵敏度 特异度 PPV NPV 训练集 CT图像特征模型 0.746 0.682～0.802 <0.001 0.595 0.815 0.667 0.764 影像组学模型 0.832 0.776～0.879 0.061 0.857 0.689 0.632 0.886 列线图 0.870 0.818～0.912 ref 0.679 0.926 0.851 0.822 验证集 CT图像特征模型 0.751 0.637～0.844 0.005 0.576 0.902 0.826 0.725 影像组学模型 0.816 0.709～0.897 0.051 0.909 0.561 0.625 0.885 列线图 0.899 0.807～0.957 ref 0.788 0.927 0.900 0.864
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