OBJECTIVE:To investigate efficiency of radiomics signature to preoperatively predict histological features of aggressive extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) with biparametric magnetic resonance imaging findings. MATERIALS AND METHODS:Sixty PTC patients with preoperative MR including T2WI and T2WI-fat-suppression (T2WI-FS) were retrospectively analyzed. Among them, 35 had ETE and 25 did not. Pre-contrast T2WI and T2WI-FS images depicting the largest section of tumor were selected. Tumor regions were manually segmented using ITK-SNAP software and 107 radiomics features were computed from the segmented regions using the open Pyradiomics package. Then, a random forest model was built to do classification in which the datasets were partitioned randomly 10 times to do training and testing with ratio of 1:1. Furthermore, forward greedy feature selection based on feature importance was adopted to reduce model overfitting. Classification accuracy was estimated on the test set using area under ROC curve (AUC). RESULTS:The model using T2WI-FS image features yields much higher performance than the model using T2WI features (AUC = 0.906 vs. 0.760 using 107 features). Among the top 10 important features of T2WI and T2WI-FS, there are 5 common features. After feature selection, the models trained using top 2 features of T2WI and the top 6 features of T2WI-FS achieve AUC 0.845 and 0.928, respectively. Combining features computed from T2WI and T2WI-FS, model performance decreases slightly (AUC = 0.882 based on all features and AUC = 0.913 based on top features after feature selection). Adjusting hyper parameters of the random forest model have negligible influence on the model performance with mean AUC = 0.907 for T2WI-FS images. CONCLUSIONS:Radiomics features based on pre-contrast T2WI and T2WI-FS is helpful to predict aggressive ETE in PTC. Particularly, the model trained using the optimally selected T2WI-FS image features yields the best classification performance. The most important features relate to lesion size and the texture heterogeneity of the tumor region.

中文翻译：

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基于双参数 MRI 的放射组学特征在术前预测甲状腺乳头状癌甲状腺外扩展的初步研究

目的：研究影像组学特征在术前预测甲状腺乳头状癌 (PTC) 侵袭性甲状腺外延伸 (ETE) 组织学特征中双参数磁共振成像结果的效率。材料与方法：回顾性分析60例术前MR包括T2WI和T2WI-脂肪抑制（T2WI-FS）的PTC患者。其中，35 人有 ETE，25 人没有。选择了描绘肿瘤最大部分的对比前 T2WI 和 T2WI-FS 图像。使用 ITK-SNAP 软件手动分割肿瘤区域，并使用开放的 Pyradiomics 包从分割的区域计算 107 个放射组学特征。然后，建立随机森林模型进行分类，将数据集随机划分10次，以1：1的比例进行训练和测试。此外，采用基于特征重要性的前向贪婪特征选择来减少模型过拟合。使用 ROC 曲线下面积 (AUC) 在测试集上估计分类准确度。结果：使用 T2WI-FS 图像特征的模型比使用 T2WI 特征的模型产生更高的性能（AUC = 0.906 vs. 0.760，使用 107 个特征）。在 T2WI 和 T2WI-FS 的 10 个重要特征中，有 5 个共同特征。特征选择后，使用T2WI的前2个特征和T2WI-FS的前6个特征训练的模型分别达到了AUC 0.845和0.928。结合从 T2WI 和 T2WI-FS 计算的特征，模型性能略有下降（基于所有特征的 AUC = 0.882，基于特征选择后的顶级特征的 AUC = 0.913）。调整随机森林模型的超参数对模型性能的影响可以忽略不计，T2WI-FS 图像的平均 AUC = 0.907。结论：基于对比前 T2WI 和 T2WI-FS 的放射组学特征有助于预测 PTC 的侵袭性 ETE。特别是，使用最佳选择的 T2WI-FS 图像特征训练的模型产生最佳分类性能。最重要的特征与病变大小和肿瘤区域的纹理异质性有关。