PDF(Pubmed)
Abstract：
OBJECTIVE: The objective of this research was to create a deep learning network that utilizes multiscale images for the classification of follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) through preoperative US.
METHODS: This retrospective study involved the collection of ultrasound images from 279 patients at two tertiary level hospitals. To address the issue of false positives caused by small nodules, we introduced a multi-rescale fusion network (MRF-Net). Four different deep learning models, namely MobileNet V3, ResNet50, DenseNet121 and MRF-Net, were studied based on the feature information extracted from ultrasound images. The performance of each model was evaluated using various metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, F1 value, receiver operating curve (ROC), area under the curve (AUC), decision curve analysis (DCA), and confusion matrix.
RESULTS: Out of the total nodules examined, 193 were identified as FTA and 86 were confirmed as FTC. Among the deep learning models evaluated, MRF-Net exhibited the highest accuracy and area under the curve (AUC) with values of 85.3% and 84.8%, respectively. Additionally, MRF-Net demonstrated superior sensitivity and specificity compared to other models. Notably, MRF-Net achieved an impressive F1 value of 83.08%. The curve of DCA revealed that MRF-Net consistently outperformed the other models, yielding higher net benefits across various decision thresholds.
CONCLUSIONS: The utilization of MRF-Net enables more precise discrimination between benign and malignant thyroid follicular tumors utilizing preoperative US.
METHODS: This retrospective study involved the collection of ultrasound images from 279 patients at two tertiary level hospitals. To address the issue of false positives caused by small nodules, we introduced a multi-rescale fusion network (MRF-Net). Four different deep learning models, namely MobileNet V3, ResNet50, DenseNet121 and MRF-Net, were studied based on the feature information extracted from ultrasound images. The performance of each model was evaluated using various metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, F1 value, receiver operating curve (ROC), area under the curve (AUC), decision curve analysis (DCA), and confusion matrix.
RESULTS: Out of the total nodules examined, 193 were identified as FTA and 86 were confirmed as FTC. Among the deep learning models evaluated, MRF-Net exhibited the highest accuracy and area under the curve (AUC) with values of 85.3% and 84.8%, respectively. Additionally, MRF-Net demonstrated superior sensitivity and specificity compared to other models. Notably, MRF-Net achieved an impressive F1 value of 83.08%. The curve of DCA revealed that MRF-Net consistently outperformed the other models, yielding higher net benefits across various decision thresholds.
CONCLUSIONS: The utilization of MRF-Net enables more precise discrimination between benign and malignant thyroid follicular tumors utilizing preoperative US.
摘要：
目的：这项研究的目的是创建一个深度学习网络，该网络利用多尺度图像通过术前US对滤泡性甲状腺癌（FTC）和滤泡性甲状腺腺瘤（FTA）进行分类。
方法：这项回顾性研究涉及收集来自两家三级医院的279名患者的超声图像。针对小结节引起的假阳性问题,我们引入了一种多尺度融合网络(MRF-Net)。四种不同的深度学习模式，即MobileNetV3、ResNet50、DenseNet121和MRF-Net，基于从超声图像中提取的特征信息进行研究。使用各种指标评估每个模型的性能，包括灵敏度,特异性，阳性预测值(PPV)，负预测值(NPV)，准确度,F1值，接收器工作曲线（ROC），曲线下面积（AUC），决策曲线分析(DCA)，和混乱矩阵。
结果：在检查的所有结节中，193个被确定为FTA，86个被确认为FTC。在评估的深度学习模型中，MRF-Net表现出最高的准确度和曲线下面积（AUC），分别为85.3％和84.8％，分别。此外,与其他模型相比，MRF-Net表现出优越的敏感性和特异性。值得注意的是,MRF-Net实现了令人印象深刻的83.08%的F1值。DCA曲线显示MRF-Net的性能始终优于其他模型，在各种决策阈值上产生更高的净收益。
结论：使用MRF-Net可以利用术前US更精确地区分良性和恶性甲状腺滤泡性肿瘤。
方法：这项回顾性研究涉及收集来自两家三级医院的279名患者的超声图像。针对小结节引起的假阳性问题,我们引入了一种多尺度融合网络(MRF-Net)。四种不同的深度学习模式，即MobileNetV3、ResNet50、DenseNet121和MRF-Net，基于从超声图像中提取的特征信息进行研究。使用各种指标评估每个模型的性能，包括灵敏度,特异性，阳性预测值(PPV)，负预测值(NPV)，准确度,F1值，接收器工作曲线（ROC），曲线下面积（AUC），决策曲线分析(DCA)，和混乱矩阵。
结果：在检查的所有结节中，193个被确定为FTA，86个被确认为FTC。在评估的深度学习模型中，MRF-Net表现出最高的准确度和曲线下面积（AUC），分别为85.3％和84.8％，分别。此外,与其他模型相比，MRF-Net表现出优越的敏感性和特异性。值得注意的是,MRF-Net实现了令人印象深刻的83.08%的F1值。DCA曲线显示MRF-Net的性能始终优于其他模型，在各种决策阈值上产生更高的净收益。
结论：使用MRF-Net可以利用术前US更精确地区分良性和恶性甲状腺滤泡性肿瘤。
