Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAF(V600E) Mutations in Papillary Thyroid Carcinoma.
Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAF(V600E) Mutations in Papillary Thyroid Carcinoma.
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DOI：
10.3389/fendo.2022.872153
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发表时间：
2022
影响因子：
5.2
通讯作者：
Qian, Xiao-qin
中科院分区：
文献类型：
--
作者：
Wang, Yu-guo;Xu, Fei-ju;Agyekum, Enock Adjei;Xiang, Hong;Wang, Yuan-dong;Zhang, Jin;Sun, Hui;Zhang, Guo-liang;Bo, Xiang-shu;Lv, Wen-zhi;Wang, Xian;Hu, Shu-dong;Qian, Xiao-qin
BRAFV600E是甲状腺癌中最常见的突变基因，与甲状腺乳头状癌（PTC）关系最为密切。我们探究了弹性超声和灰度超声在预测PTC中BRAFV600E突变方面的价值。
回顾性分析了2014年1月至2021年间138例术前行超声检查的PTC患者。患者被分为BRAFV600E无突变组（n = 75）和BRAFV600E突变组（n = 63）。患者被随机分为训练组（n = 96）和测试组（n = 42）。从灰度超声和弹性超声图像中共提取了479个影像组学特征。通过回归分析筛选出信息量最大的特征。然后，采用10折交叉验证比较不同分类算法的性能。运用逻辑回归预测BRAFV600E突变。
从灰度超声图像中提取出8个影像组学特征，从弹性超声图像中提取出5个影像组学特征。利用这些影像组学特征构建了3种模型。这3种模型分别源自弹性超声、灰度超声以及灰度超声与弹性超声的结合，在训练数据集中的曲线下面积（AUC）分别为0.952 [95%置信区间（CI），0.914 - 0.990]、0.792 [95% CI，0.703 - 0.882]和0.985 [95% CI，0.965 - 1.000]，在测试数据集中的AUC分别为0.931 [95% CI，0.841 - 1.000]、0.725 [95% CI，0.569 - 0.880]和0.938 [95% CI，0.851 - 1.000]。
基于灰度超声和弹性超声的影像组学模型对PTC患者BRAFV600E基因突变具有良好的预测价值。
BRAFV600E is the most common mutated gene in thyroid cancer and is most closely related to papillary thyroid carcinoma(PTC). We investigated the value of elasticity and grayscale ultrasonography for predicting BRAFV600E mutations in PTC. 138 patients with PTC who underwent preoperative ultrasound between January 2014 and 2021 were retrospectively examined. Patients were divided into BRAFV600E mutation-free group (n=75) and BRAFV600E mutation group (n=63). Patients were randomly divided into training (n=96) and test (n=42) groups. A total of 479 radiomic features were extracted from the grayscale and elasticity ultra-sonograms. Regression analysis was done to select the features that provided the most information. Then, 10-fold cross-validation was used to compare the performance of different classification algorithms. Logistic regression was used to predict BRAFV600E mutations. Eight radiomics features were extracted from the grayscale ultrasonogram, and five radiomics features were extracted from the elasticity ultrasonogram. Three models were developed using these radiomic features. The models were derived from elasticity ultrasound, grayscale ultrasound, and a combination of grayscale and elasticity ultrasound, with areas under the curve (AUC) 0.952 [95% confidence interval (CI), 0.914−0.990], AUC 0.792 [95% CI, 0.703−0.882], and AUC 0.985 [95% CI, 0.965−1.000] in the training dataset, AUC 0.931 [95% CI, 0.841−1.000], AUC 0. 725 [95% CI, 0.569−0.880], and AUC 0.938 [95% CI, 0.851−1.000] in the test dataset, respectively. The radiomic model based on grayscale and elasticity ultrasound had a good predictive value for BRAFV600E gene mutations in patients with PTC.
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影响因子：
3.7
作者：
Yoon, Jung Hyun;Han, Kyunghwa;Kwak, Jin Young
通讯作者：
Kwak, Jin Young
影响因子：
--
作者：
Hahn SY;Kim TH;Ki CS;Kim SW;Ahn S;Shin JH;Chung JH
通讯作者：
Chung JH
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DOI：
10.1007/s11548-018-1796-5
发表时间：
2018-10-01
影响因子：
3
作者：
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通讯作者：
Cui, Ligang
影响因子：
5.2
作者：
Xu, Jun-Mei;Chen, Yong-Jun;Chen, Man
通讯作者：
Chen, Man
影响因子：
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作者：
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