Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
Department of Computational Science and Engineering, Yonsei University, Seoul, South Korea.
Eur Radiol. 2021 Jul;31(7):5059-5067. doi: 10.1007/s00330-020-07670-3. Epub 2021 Jan 18.
The purpose of this study was to evaluate the role of the radiomics score using US images to predict malignancy in AUS/FLUS and FN/SFN nodules.
One hundred fifty-five indeterminate thyroid nodules in 154 patients who received initial US-guided FNA for diagnostic purposes were included in this retrospective study. A representative US image of each tumor was acquired, and square ROIs covering the whole nodule were drawn using the Paint program of Windows 7. Texture features were extracted by in-house texture analysis algorithms implemented in MATLAB 2019b. The LASSO logistic regression model was used to choose the most useful predictive features, and ten-fold cross-validation was performed. Two prediction models were constructed using multivariable logistic regression analysis: one based on clinical variables, and the other based on clinical variables with the radiomics score. Predictability of the two models was assessed with the AUC of the ROC curves.
Clinical characteristics did not significantly differ between malignant and benign nodules, except for mean nodule size. Among 730 candidate texture features generated from a single US image, 15 features were selected. Radiomics signatures were constructed with a radiomics score, using selected features. In multivariable logistic regression analysis, higher radiomics score was associated with malignancy (OR = 10.923; p < 0.001). The AUC of the malignancy prediction model composed of clinical variables with the radiomics score was significantly higher than the model composed of clinical variables alone (0.839 vs 0.583).
Quantitative US radiomics features can help predict malignancy in thyroid nodules with indeterminate cytology.
本研究旨在评估使用超声图像的放射组学评分预测 AUS/FLUS 和 FN/SFN 结节恶性的作用。
本回顾性研究纳入了 154 例因诊断目的接受初始超声引导下细针抽吸活检（FNA）的 155 个不确定甲状腺结节。获取每个肿瘤的有代表性的超声图像，并使用 Windows 7 的 Paint 程序在整个结节上绘制方形 ROI。使用 MATLAB 2019b 中实现的内部纹理分析算法提取纹理特征。LASSO 逻辑回归模型用于选择最有用的预测特征，并进行十折交叉验证。使用多变量逻辑回归分析构建了两个预测模型：一个基于临床变量，另一个基于临床变量和放射组学评分。使用 ROC 曲线的 AUC 评估两个模型的预测能力。
恶性和良性结节之间的临床特征除了平均结节大小外，没有显著差异。在从单个超声图像生成的 730 个候选纹理特征中，选择了 15 个特征。使用选定的特征构建了放射组学特征的放射组学评分。在多变量逻辑回归分析中，较高的放射组学评分与恶性肿瘤相关（OR=10.923；p<0.001）。由临床变量和放射组学评分组成的恶性肿瘤预测模型的 AUC 显著高于仅由临床变量组成的模型（0.839 比 0.583）。
定量超声放射组学特征可帮助预测细胞学不确定的甲状腺结节的恶性。
