使用机器学习(ML)模型分析桥本甲状腺炎(HT)合并甲状腺乳头状癌(PTC)患者甲状腺肿瘤二维超声图像提取的影像和临床特征,并探讨该方法在颈淋巴结转移(LNM)术前无创鉴定中的诊断性能。
■共纳入528例HT合并PTC患者,根据其是否存在LNM的病理结果分为两组。这些组随后被指定为具有LNM组和没有LNM组。三名超声医生独立地描绘了感兴趣的区域并提取了放射学特征。两种模式,影像组学特征和影像组学临床特征,用于构建随机森林(RF),支持向量机(SVM),LightGBM,K最近邻(KNN),和XGBoost模型。通过测试数据集上的接收器工作特性(ROC)曲线评估了这五个ML模型在两种模式下的性能,Shapley加法扩张(SHAP)用于模型可视化。
■所有五个ML模型都显示出良好的性能,ROC曲线下面积(AUC)范围为0.798至0.921。LightGBM和XGBoost展示了最佳性能,优于其他模型(P<0.05)。用影像组学-临床特征构建的ML模型比仅使用影像组学特征构建的模型表现更好(P<0.05)。性能最好的模型的SHAP可视化表明前后直径,超下直径,original_shape_VoxelVolume,年龄,小波-LHL_firstorder_10百分位,从左到右的直径对LightGBM模型的影响最显著。另一方面,超下直径,前后直径,从左到右的直径,original_shape_VoxelVolume,original_firstorder_InterquartileRange,和年龄对XGBoost模型的影响最显著。
■基于影像组学和临床特征的ML模型可以准确评估HT合并PTC患者的颈淋巴结状态。在5个ML模型中,LightGBM和XGBoost展示了最佳的评估性能。
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.