Value of ultrasonography combined with model in predicting ipsilateral central cervical lymph node metastasis in papillary thyroid carcinoma
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摘要:
目的 探讨超声检查联合模型预测甲状腺乳头状癌（PTC）同侧中央区颈部淋巴结转移（CLNM）的价值。 方法 回顾性分析南方医科大学南方医院2021年1~7月137例经病理证实的PTC患者的临床资料及术前甲状腺超声二维影像，根据术后病理结果将患者分为转移组65例，非转移组72例。所有患者均淋巴结行预防性中央区淋巴结清扫，并根据术后病理结果分为转移组和非转移组。在超声影像中手动勾画病变，从处理后的超声图像中导出了纹理特征。然后使用ICC、统计筛选、相关系数筛选以及LASSO方法，最终将LASSO筛选的非0特征作为输入，进行影像特征模型建模。将137例患者的临床有效信息构建与影像特征模型相同的临床特征模型。将影像特征与临床特征相结合，构建联合模型。 结果 在影像特征模型中，ExtraTrees模型表现最佳，训练集和测试集的曲线下面积分别为0.895和0.836。临床特征的最优模型也是ExtraTrees模型，训练集和测试集的曲线下面积分别为0.843和0.701。而联合模型的预测能力最好，训练集和测试集的曲线下面积分别为0.900和0.854。 结论 结合影像特征和临床特征的联合模型对PTC同侧CLNM的预测能力较好，可为临床决策提供一种无创、有效的方法。 Abstract:Objective To investigate the value of ultrasound combined model in predicting ipsilateral central cervical lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC). Methods The clinical data and preoperative two-dimensional ultrasound images of 137 patients with pathologically confirmed PTC in Nanfang Hospital of Southern Medical University from January 2021 to July 2021 were retrospectively analyzed, and they were were divided into metastatic group (n=65) and non-metastatic group (n=72) by postoperative pathological results. All patients underwent prophylactic central lymph node dissection. The lesions were delineated manually in the ultrasound images, and the texture features were derived from the processed ultrasound images. Then ICC, statistical screening, correlation coefficient screening and LASSO method were used, and the non-0 features filtered by LASSO were used as input to build the image feature model. 137 patients' clinically effective information was used to construct the same clinical feature model as the image feature model. A combined model was constructed by combining imaging features with clinical features. Results Among the image feature models, the ExtraTrees model has the best performance, and the AUC of the training set and the test set are 0.895 and 0.836 respectively. The optimal model for clinical features is also the ExtraTrees model, with AUC of 0.843 and 0.701 in the training and test sets, respectively. The combined model has the best predictive ability, with AUC of 0.900 and 0.854 for the training set and test set, respectively. Conclusion The combined model combining imaging features and clinical features has a good ability to predict CLNM in the ipsilateral central region of PTC, and it can provide a non-invasive and effective method for clinical decision-making. -
Key words:
- thyroid papillary carcinoma /
- lymph node metastasis /
- imaging omics /
- texture analysis
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图 1 超声显示PTC及手动勾画ROI
Figure 1. Thyroid papillary carcinoma was shown on ultrasound and manual sketching ROI.
图 3 LASSO的特征选择
Figure 3. Feature selection for LASSO. A: Coefficients of 10 fold cross validation; B: MSE of 10 fold cross validation; C: The histogram of the Rad-score based on the selected feature.
图 4 不同模型在训练队列(A)和测试队列(B)的ROC曲线
Figure 4. ROC curves of different models in the training(A)and testing sets(B).
图 5 临床特征的单变量分析及Spearman相关系数分析的结果
Figure 5. Results of univariate analysis and Spearman correlation coefficient analysis of clinical features. A: Univariate analysis of clinical features; B: Correlations between each clinical features, showed that age had maximum correlation coefficient.
图 6 训练队列（A)和测试队列（B)中不同模型的ROC曲线
Figure 6. ROC curves of different models in the training(A) and testing sets(B).
图 8 临床特征模型、影像特征模型和联合模型性能差异性的Delong检验结果
Figure 8. Delong test results for performance differences among clinical feature models, imaging feature models, and combined model.
图 9 训练队列（A）和测试队列（B)中的校准曲线
Figure 9. Calibration curve in training (A) and test (B) cohort.
表 1 两组患者队列的基线特征
Table 1. Baseline characteristics of patients oftewo groups in cohorts
Index Non-metastatic group（n=48） Metastatic group（n=47） P Non-metastatic group（n=24） Metastatic group（n=18） P Age (year, Mean±SD) 43.56±11.47 37.11±11.93 0.010 42.38±12.05 36.56±14.48 0.162 Gender [n(%)] 0.476 0.819 Female 31(64.58) 26(55.32) 14(58.33) 12(66.67) Male 17(35.42) 21(44.68) 10(41.67) 6(33.33) 
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表 2 不同机器学习算法在训练队列和测试队列中的模型性能
Table 2. Model performance of different machine learning algorithms in the training and testing sets.
Model_name Accuracy AUC 95% CI Sensitivity Specificity Positive predictive value Negative predictive value Task LR 0.768 0.850 0.7747-0.9257 0.809 0.729 0.745 0.795 Train LR 0.714 0.764 0.6197-0.9081 0.611 0.792 0.687 0.731 Test SVM 0.853 0.948 0.9101-0.9861 0.936 0.771 0.800 0.925 Train SVM 0.738 0.824 0.6979-0.9502 0.722 0.750 0.684 0.783 Test RF 0.916 0.984 0.9681-1.0000 0.957 0.875 0.882 0.955 Train RF 0.738 0.806 0.6726-0.9385 0.833 0.667 0.652 0.842 Test ET 0.811 0.895 0.8345-0.9554 0.851 0.771 0.784 0.841 Train ET 0.762 0.836 0.7146-0.9567 0.833 0.708 0.682 0.850 Test XGBoost 0.884 0.951 0.9134-0.9882 0.936 0.833 0.846 0.930 Train XGBoost 0.714 0.730 0.5722 - 0.8885 0.389 0.958 0.875 0.676 Test LightGBM 0.800 0.881 0.8151-0.9468 0.660 0.937 0.912 0.738 Train LightGBM 0.714 0.742 0.5838-0.9000 0.611 0.792 0.687 0.731 Test MLP 0.800 0.886 0.8234-0.9488 0.681 0.917 0.889 0.746 Train MLP 0.738 0.787 0.6470-0.9270 0.778 0.708 0.667 0.810 Test LR: Linear regression; SVM: Support vector machine; RF: Random forest; ET: Extra trees; MLP: Multilayer perceptron.
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表 3 临床特征的单变量分析结果
Table 3. Univariable analysis of clinical features
Index OR OR lower 95% CI OR upper 95% CI P Age 0.989 0.982 0.996 0.008 Gender 1.101 0.924 1.313 0.362
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表 4 不同机器学习算法在训练队列和测试队列中的模型性能
Table 4. Model performance of different machine learning algorithms in the training and testing sets
Model_name Accuracy AUC 95% CI Sensitivity Specificity PPV NPV Task LR 0.653 0.652 0.5397-0.7639 0.625 0.681 0.667 0.640 Train LR 0.571 0.611 0.4329-0.7893 0.542 0.611 0.650 0.500 Test SVM 0.642 0.641 0.5259-0.7556 0.646 0.638 0.646 0.638 Train SVM 0.548 0.632 0.4533-0.8106 0.542 0.556 0.619 0.476 Test RF 0.747 0.790 0.6973- 0.8829 0.792 0.702 0.731 0.767 Train RF 0.643 0.677 0.5038-0.8504 0.625 0.667 0.714 0.571 Test ET 0.768 0.843 0.7660-0.9206 0.812 0.723 0.750 0.791 Train ET 0.619 0.701 0.5369-0.8659 0.667 0.556 0.667 0.556 Test XGBoost 0.747 0.817 0.7328-0.9019 0.792 0.702 0.731 0.767 Train XGBoost 0.595 0.685 0.5155- 0.8548 0.667 0.500 0.640 0.529 Test LightGBM 0.663 0.688 0.5814-0.7945 0.729 0.596 0.648 0.683 Train LightGBM 0.571 0.597 0.4152- 0.7793 0.583 0.556 0.636 0.500 Test MLP 0.505 0.627 0.5139-0.7406 1.000 0.000 0.505 0.000 Train MLP 0.571 0.537 0.3577-0.7164 1.000 0.000 0.571 0.000 Test
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表 5 临床特征模型、影像特征模型和联合模型的评估指标
Table 5. Evaluation metrics for clinical feature models, imaging feature models, and combined model
Signature Accuracy AUC 95% CI Sensitivity Specificity PPV NPV Cohort Clinic_Sig 0.768 0.843 0.7660-0.9206 0.812 0.723 0.750 0.791 Train Rad_Sig 0.811 0.895 0.8345-0.9554 0.771 0.851 0.841 0.784 Train Nomogram 0.811 0.900 0.8406-0.9591 0.812 0.809 0.812 0.809 Train Clinic_Sig 0.619 0.701 0.5369-0.8659 0.667 0.556 0.667 0.556 Test Rad_Sig 0.714 0.836 0.7146-0.9567 0.750 0.667 0.750 0.667 Test Nomogram 0.786 0.854 0.7392-0.9691 0.792 0.778 0.826 0.737 Test
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