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Abstract：
UNASSIGNED: To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients.
UNASSIGNED: A total of 271 US videos from 271 early breast cancer patients collected from Xiang\'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models.
UNASSIGNED: Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers\' review.
UNASSIGNED: A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.
UNASSIGNED: A total of 271 US videos from 271 early breast cancer patients collected from Xiang\'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models.
UNASSIGNED: Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers\' review.
UNASSIGNED: A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.
摘要：
■开发一种深度学习（DL）模型，用于在乳腺癌患者中使用动态超声（US）视频预测腋窝淋巴结（ALN）转移。
■从2019年9月至2021年6月，厦门大学湘安医院和汕头市中心医院收集的271个早期乳腺癌患者的271个美国视频作为培训，验证,和内部测试集（测试集A）。此外,来自49名乳腺癌患者的49个美国视频的独立数据集，自2021年7月至2022年5月从同济大学上海第十医院收集，用作外部测试集（测试集B）。所有ALN转移均经病理检查证实。三个不同的卷积神经网络（CNN）与R2+1D，TIN,和ResNet-3D架构用于构建模型。将美国视频DL模型的性能与超声检查者进行的美国静态图像DL模型和腋窝US检查的性能进行了比较。基于准确性评估了DL模型和超声扫描仪的性能，灵敏度,特异性，和接受者工作特征曲线下面积(AUC)。此外,梯度类激活映射(Grad-CAM)技术也被用来增强模型的可解释性。
■在三种美国视频DL模型中，TIN表现最好，在测试集A中预测ALN转移的AUC为0.914（95％CI：0.843-0.985）。该模型的准确性为85.25％（52/61），敏感性为76.19%(16/21)，特异性为90.00%(36/40)。US视频DL模子的AUC优于US静态图象DL模子(0.856,95%CI：0.753-0.959,P<0.05)。Grad-CAM技术确认了模型的热图，突出显示了关键帧的重要子区域，供超声检查者回顾。
■开发了一种可行且改进的DL模型来预测来自乳腺癌US视频图像的ALN转移。本研究中具有可靠可解释性的DL模型将为早期乳腺癌患者的腋窝的适当管理提供早期诊断策略。
■从2019年9月至2021年6月，厦门大学湘安医院和汕头市中心医院收集的271个早期乳腺癌患者的271个美国视频作为培训，验证,和内部测试集（测试集A）。此外,来自49名乳腺癌患者的49个美国视频的独立数据集，自2021年7月至2022年5月从同济大学上海第十医院收集，用作外部测试集（测试集B）。所有ALN转移均经病理检查证实。三个不同的卷积神经网络（CNN）与R2+1D，TIN,和ResNet-3D架构用于构建模型。将美国视频DL模型的性能与超声检查者进行的美国静态图像DL模型和腋窝US检查的性能进行了比较。基于准确性评估了DL模型和超声扫描仪的性能，灵敏度,特异性，和接受者工作特征曲线下面积(AUC)。此外,梯度类激活映射(Grad-CAM)技术也被用来增强模型的可解释性。
■在三种美国视频DL模型中，TIN表现最好，在测试集A中预测ALN转移的AUC为0.914（95％CI：0.843-0.985）。该模型的准确性为85.25％（52/61），敏感性为76.19%(16/21)，特异性为90.00%(36/40)。US视频DL模子的AUC优于US静态图象DL模子(0.856,95%CI：0.753-0.959,P<0.05)。Grad-CAM技术确认了模型的热图，突出显示了关键帧的重要子区域，供超声检查者回顾。
■开发了一种可行且改进的DL模型来预测来自乳腺癌US视频图像的ALN转移。本研究中具有可靠可解释性的DL模型将为早期乳腺癌患者的腋窝的适当管理提供早期诊断策略。
