PDF(Pubmed)
Abstract：
UNASSIGNED: The incidence rate of thyroid nodules has reached 65%, but only 5-15% of these modules are malignant. Therefore, accurately determining the benign and malignant nature of thyroid nodules can prevent unnecessary treatment. We aimed to develop a deep-learning (DL) radiomics model based on ultrasound (US), explore its diagnostic efficacy for benign and malignant thyroid nodules, and verify whether it improved the diagnostic level of physicians.
UNASSIGNED: We retrospectively included 1,076 thyroid nodules from 817 patients at three institutions. The radiomics and DL features of the US images were extracted and used to construct radiomics signature (Rad_sig) and deep-learning signature (DL_sig). A Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used for feature selection. Clinical US semantic signature (C_US_sig) was constructed based on clinical information and US semantic features. Next, a combined model was constructed based on the above three signatures in the form of a nomogram. The model was constructed using a development set (institution 1: 719 nodules), and the model was evaluated using two external validation sets (institution 2: 74 nodules, and institution 3: 283 nodules). The performance of the model was assessed using decision curve analysis (DCA) and calibration curves. Furthermore, the C_US_sigs of junior physicians, senior physicians, and expers were constructed. The DL radiomics model was used to assist the physicians with different levels of experience in the interpretation of thyroid nodules.
UNASSIGNED: In the development and validation sets, the combined model showed the highest performance, with areas under the curve (AUCs) of 0.947, 0.917, and 0.929, respectively. The DCA results showed that the comprehensive nomogram had the best clinical utility. The calibration curves indicated good calibration for all models. The AUCs for distinguishing between benign and malignant thyroid nodules by junior physicians, senior physicians, and experts were 0.714-0.752, 0.740-0.824, and 0.891-0.908, respectively; however, with the assistance of DL radiomics, the AUCs reached 0.858-0.923, 0.888-0.944, and 0.912-0.919, respectively.
UNASSIGNED: The nomogram based on DL radiomics had high diagnostic efficacy for thyroid nodules, and DL radiomics could assist physicians with different levels of experience to improve their diagnostic level.
UNASSIGNED: We retrospectively included 1,076 thyroid nodules from 817 patients at three institutions. The radiomics and DL features of the US images were extracted and used to construct radiomics signature (Rad_sig) and deep-learning signature (DL_sig). A Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used for feature selection. Clinical US semantic signature (C_US_sig) was constructed based on clinical information and US semantic features. Next, a combined model was constructed based on the above three signatures in the form of a nomogram. The model was constructed using a development set (institution 1: 719 nodules), and the model was evaluated using two external validation sets (institution 2: 74 nodules, and institution 3: 283 nodules). The performance of the model was assessed using decision curve analysis (DCA) and calibration curves. Furthermore, the C_US_sigs of junior physicians, senior physicians, and expers were constructed. The DL radiomics model was used to assist the physicians with different levels of experience in the interpretation of thyroid nodules.
UNASSIGNED: In the development and validation sets, the combined model showed the highest performance, with areas under the curve (AUCs) of 0.947, 0.917, and 0.929, respectively. The DCA results showed that the comprehensive nomogram had the best clinical utility. The calibration curves indicated good calibration for all models. The AUCs for distinguishing between benign and malignant thyroid nodules by junior physicians, senior physicians, and experts were 0.714-0.752, 0.740-0.824, and 0.891-0.908, respectively; however, with the assistance of DL radiomics, the AUCs reached 0.858-0.923, 0.888-0.944, and 0.912-0.919, respectively.
UNASSIGNED: The nomogram based on DL radiomics had high diagnostic efficacy for thyroid nodules, and DL radiomics could assist physicians with different levels of experience to improve their diagnostic level.
摘要：
■甲状腺结节的发病率达到65%，但是这些模块中只有5-15%是恶性的。因此,准确判断甲状腺结节的良恶性可以防止不必要的治疗。我们旨在开发基于超声（US）的深度学习（DL）影像组学模型，探讨其对甲状腺良恶性结节的诊断效能,并验证其是否提高了医师的诊断水平。
■我们回顾性纳入了三个机构的817名患者的1,076个甲状腺结节。提取了美国图像的影像组学和DL特征，并将其用于构建影像组学签名（Rad_sig）和深度学习签名（DL_sig）。Pearson相关性分析和最小绝对收缩和选择算子（LASSO）回归分析用于特征选择。基于临床信息和US语义特征构建临床US语义签名(C_US_sig)。接下来,基于上述三个特征,以列线图的形式构建了组合模型.该模型是使用开发集(机构1:719结核)构建的，并使用两个外部验证集(机构2：74个结节，和机构3：283结节)。使用决策曲线分析(DCA)和校准曲线评估模型的性能。此外，初级医生的C_US_sig，高级医师,并构建了实验。DL影像组学模型用于帮助具有不同经验水平的医师解释甲状腺结节。
■在开发和验证集中，组合模型表现出最高的性能，曲线下面积（AUC）分别为0.947、0.917和0.929。DCA结果表明，综合列线图具有最佳的临床实用性。校准曲线表明所有模型的校准良好。初级医师区分良性和恶性甲状腺结节的AUC，高级医师,和专家分别为0.714-0.752、0.740-0.824和0.891-0.908；然而，在DL影像组学的协助下，AUC分别达到0.858-0.923、0.888-0.944和0.912-0.919。
■基于DL影像组学的列线图对甲状腺结节具有较高的诊断效能，和DL影像组学可以帮助具有不同经验水平的医生提高诊断水平。
■我们回顾性纳入了三个机构的817名患者的1,076个甲状腺结节。提取了美国图像的影像组学和DL特征，并将其用于构建影像组学签名（Rad_sig）和深度学习签名（DL_sig）。Pearson相关性分析和最小绝对收缩和选择算子（LASSO）回归分析用于特征选择。基于临床信息和US语义特征构建临床US语义签名(C_US_sig)。接下来,基于上述三个特征,以列线图的形式构建了组合模型.该模型是使用开发集(机构1:719结核)构建的，并使用两个外部验证集(机构2：74个结节，和机构3：283结节)。使用决策曲线分析(DCA)和校准曲线评估模型的性能。此外，初级医生的C_US_sig，高级医师,并构建了实验。DL影像组学模型用于帮助具有不同经验水平的医师解释甲状腺结节。
■在开发和验证集中，组合模型表现出最高的性能，曲线下面积（AUC）分别为0.947、0.917和0.929。DCA结果表明，综合列线图具有最佳的临床实用性。校准曲线表明所有模型的校准良好。初级医师区分良性和恶性甲状腺结节的AUC，高级医师,和专家分别为0.714-0.752、0.740-0.824和0.891-0.908；然而，在DL影像组学的协助下，AUC分别达到0.858-0.923、0.888-0.944和0.912-0.919。
■基于DL影像组学的列线图对甲状腺结节具有较高的诊断效能，和DL影像组学可以帮助具有不同经验水平的医生提高诊断水平。
