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基于影像组学和临床特征的机器学习术前评估桥本甲状腺炎合并甲状腺乳头状癌颈部淋巴结转移的初步研究

Preoperative Evaluation of Cervical Lymph Node Metastasis in Patients With Hashimoto's Thyroiditis Combined With Thyroid Papillary Carcinoma Using Machine Learning and Radiomics-Based Features: A Preliminary Study

    摘要
  • 摘要:
    目的 利用机器学习（machine learning, ML）模型分析桥本甲状腺炎（Hashimoto thyroiditis, HT）合并甲状腺乳头状癌（papillary thyroid carcinoma, PTC）患者的甲状腺肿瘤的二维超声图像提取的影像组学和临床特征，探讨其术前无创识别该类患者颈部淋巴结转移（lymph node metastasis, LNM）的能力。
    方法 纳入HT合并PTC患者528例，以病理结果为金标准划分为存在颈部淋巴结转移组和不存在颈部淋巴结转移组，由3名医生独立勾画感兴趣区，提取感兴趣区的影像组学特征，以影像组学特征和影像组学特征结合临床特征2种模式构建随机森林（random forest, RF）、支持向量机（support vector machine, SVM）、LightGBM、K邻近算法（K-nearest neighbor, KNN）和XGBoost模型，在测试集上绘制受试者操作特征（receiver operating characteristic, ROC）曲线评价5种机器学习模型的2种模式的性能，并使用SHapley可加性解释（SHapley Additive exPlanations, SHAP）对模型进行可视化。
    结果 5种机器学习模型均具有较好的性能，ROC曲线下面积（area under curve, AUC）为0.798～0.921，其中LightGBM和XGBoost性能最佳，优于其他模型（P<0.05）。影像组学特征结合临床特征构建的机器学习模型优于仅使用影像组学特征构建的模型（P<0.05）。SHAP对性能最佳的模型可视化表明，前后径、上下径、original_shape_VoxelVolume、年龄、wavelet-LHL_firstorder_10Percentile和左右径对LightGBM的影响最显著；上下径、前后径、左右径、original_shape_VoxelVolume、original_firstorder_InterquartileRange和年龄对XGBoost的影响最显著。
    结论 基于影像组学和临床特征的机器学习模型能够准确地评估HT合并PTC患者颈部淋巴结状态。在5种机器学习模型中，LightGBM和XGBoost的评估性能最佳。

     

    Abstract:
    Objective To analyze the radiomic and clinical features extracted from 2D ultrasound images of thyroid tumors in patients with Hashimoto's thyroiditis (HT) combined with papillary thyroid carcinoma (PTC) using machine learning (ML) models, and to explore the diagnostic performance of the method in making preoperative noninvasive identification of cervical lymph node metastasis (LNM).
    Methods A total of 528 patients with HT combined with PTC were enrolled and divided into two groups based on their pathological results of the presence or absence of LNM. The groups were subsequently designated the With LNM Group and the Without LNM Group. Three ultrasound doctors independently delineated the regions of interest and extracted radiomic features. Two modes, radiomic features and radiomics-clinical features, were used to construct random forest (RF), support vector machine (SVM), LightGBM, K-nearest neighbor (KNN), and XGBoost models. The performance of these five ML models in the two modes was evaluated by the receiver operating characteristic (ROC) curves on the test dataset, and SHapley Additive exPlanations (SHAP) was used for model visualization.
    Results All five ML models showed good performance, with area under the ROC curve (AUC) ranging from 0.798 to 0.921. LightGBM and XGBoost demonstrated the best performance, outperforming the other models (P<0.05). The ML models constructed with radiomics-clinical features performed better than those constructed using only radiomic features (P<0.05). The SHAP visualization of the best-performing models indicated that the anteroposterior diameter, superoinferior diameter, original_shape_VoxelVolume, age, wavelet-LHL_firstorder_10Percentile, and left-to-right diameter had the most significant effect on the LightGBM model. On the other hand, the superoinferior diameter, anteroposterior diameter, left-to-right diameter, original_shape_VoxelVolume, original_firstorder_InterquartileRange, and age had the most significant effect on the XGBoost model.
    Conclusion ML models based on radiomics and clinical features can accurately evaluate the cervical lymph node status in patients with HT combined with PTC. Among the 5 ML models, LightGBM and XGBoost demonstrate the best evaluation performance.

     

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