PDF(Pubmed)
Abstract：
Objective: To explore extrathyroidal extension (ETE) in children and adolescents with papillary thyroid carcinoma using a multiclassifier ultrasound radiomic model. Methods: In this study, data from 164 pediatric patients with papillary thyroid cancer (PTC) were retrospectively analyzed and patients were randomly divided into a training cohort (115) and a validation cohort (49) in a 7:3 ratio. To extract radiomics features from ultrasound images of the thyroid, areas of interest (ROIs) were delineated layer by layer along the edge of the tumor contour. The feature dimension was then reduced using the correlation coefficient screening method, and 16 features with a nonzero coefficient were chosen using Lasso. Then, in the training cohort, four supervised machine learning radiomics models (k-nearest neighbor, random forest, support vector machine [SVM], and LightGBM) were developed. ROC and decision-making curves were utilized to compare model performance, which was validated using validation cohorts. In addition, the SHapley Additive exPlanations (SHAP) framework was applied to explain the optimal model. Results: In the training cohort, the average area under the curve (AUC) was 0.880 (0.835-0.927), 0.873 (0.829-0.916), 0.999 (0.999-1.000), and 0.926 (0.892-0.926) for the SVM, KNN, random forest, and LightGBM, respectively. In the validation cohort, the AUC for the SVM was 0.784 (0.680-0.889), for the KNN, it was 0.720 (0.615-0.825), for the random forest, it was 0.728 (0.622-0.834), and for the LightGBM, it was 0.832 (0.742-0.921). Generally, the LightGBM model performed well in both the training and validation cohorts. From the SHAP results, original_shape_MinorAxisLength,original_shape_Maximum2DDiameterColumn, and wavelet-HHH_glszm_SmallAreaLowGrayLevelEmphasis have the most significant effect on the model. Conclusions: Our combined model based on machine learning and ultrasonic radiomics demonstrate the excellent predictive ability for extrathyroidal extension (ETE) in pediatric PTC.
摘要：
目的：应用多分类器超声影像组学模型探讨儿童和青少年甲状腺乳头状癌的甲状腺外延伸（ETE）。方法:在本研究中,我们对164例甲状腺乳头状癌(PTC)儿科患者的数据进行了回顾性分析,并以7∶3的比例将患者随机分为训练队列(115)和验证队列(49).为了从甲状腺的超声图像中提取影像组学特征，沿着肿瘤轮廓的边缘逐层描绘感兴趣区域(ROI)。然后使用相关系数筛选方法降低特征维数，使用Lasso选择了16个系数为非零的特征。然后，在训练组中，四个有监督的机器学习影像组学模型(k-最近邻，随机森林,支持向量机[SVM]，和LightGBM）的开发。ROC和决策曲线用于比较模型性能，已使用验证队列进行验证。此外,运用沙普利加性扩张(SHAP)框架对最优模型进行了解释。结果：在训练队列中，平均曲线下面积(AUC)为0.880(0.835-0.927),0.873(0.829-0.916),0.999（0.999-1.000），SVM为0.926(0.892-0.926)，KNN,随机森林,和LightGBM，分别。在验证队列中，SVM的AUC为0.784（0.680-0.889），对于KNN来说,它是0.720（0.615-0.825），对于随机森林，它是0.728（0.622-0.834），对于LightGBM，它是0.832（0.742-0.921）。一般来说,LightGBM模型在训练和验证队列中均表现良好。从SHAP结果来看，original_shape_MinorAxisLength,original_shape_Maximum2DDiameterColumn,和小波-HHH_glszm_SmallAreaLowGrayLevel强调对模型的影响最显著。结论：我们基于机器学习和超声影像组学的组合模型对小儿PTC的甲状腺外延伸（ETE）具有出色的预测能力。
