| 李金阳,张羽萌,张超.平扫CT深度学习模型预测经保守治疗后排出输尿管结石[J].中国医学影像技术,2023,39(8):1225~1228 |
| 平扫CT深度学习模型预测经保守治疗后排出输尿管结石 |
| Deep learning models based on plain scan CT for predicting discharge of ureteral calculus after conservative management |
| 投稿时间：2023-05-29 修订日期：2023-07-08 |
| DOI：10.13929/j.issn.1003-3289.2023.08.023 |
| 中文关键词: 输尿管结石 神经网络,计算机 深度学习 体层摄影术,X线计算机 |
| 英文关键词:ureteral calculi neural networks, computer deep learning tomography, X-ray computed |
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| 中文摘要: |
| 目的 观察基于平扫CT建立的神经网络深度学习(DL)模型预测保守促排石治疗后排出输尿管结石的价值。方法 纳入915例接受保守促排石治疗的输尿管结石患者,随机分为训练集(n=700)、验证集(n=100)及测试集(n=115)。基于平扫CT标记结石三维形状,分别针对训练集和验证集获取三维卷积神经网络(3D-CNN)、二维卷积神经网络(2D-CNN)及全连接神经网络(FCN)最佳参数并建立模型,以测试集检测模型预测能力;绘制受试者工作特征曲线,比较各模型及结石最大径预测测试集经保守治疗后可否排出输尿管结石的效能。结果 915例中,229例经保守治疗后排出输尿管结石。3D-CNN模型预测测试集排出输尿管结石的效能最佳,其曲线下面积(AUC)为0.956,高于2D-CNN模型(0.865)、FCN模型(0.813)及结石直径(0.818)(P均＜0.01);2D-CNN模型预测AUC高于FCN模型及结石直径(P均＜0.05)。结论 利用DL模型、尤其3D-CNN能准确预测输尿管结石可否于保守治疗后排出。 |
| 英文摘要: |
| Objective To observe the value of deep learning (DL) models established based on plain CT for predicting discharge of ureteral calculus after conservative management. Methods Totally 915 patients with single ureteral calculus who underwent medical expulsive therapy were enrolled. The patients were randomly divided into training set (n=700), validation set (n=100) or test set (n=115). The three-dimensional shape of calculus was marked on plain CT images, and the optimal parameter models of three-dimensional convolutional neural network (3D-CNN), two-dimensional convolutional neural network (2D-CNN) and fully-connected network (FCN) were obtained based on data of training set and verification set. Then receiver operating characteristic curves were drawn, and the efficacies of the models and the maximum diameter of calculus for predicting whether it could be discharged after conservative management were compared. Results Among 915 cases, ureteral calculus was discharged in 229 cases after conservative management. 3D-CNN model was the best for predicting whether ureteral calculus could be discharged after conservative management, with the area under the curve (AUC) of 0.956, higher than that of 2D-CNN model (0.865), FCN model (0.813) and calculus diameter (0.818) (all P＜0.01). Meanwhile, the AUC of 2D-CNN model was higher than that of FCN model and calculus diameter (both P＜0.05). Conclusion DL models, especially 3D-CNN, could be used to accurately predict whether ureteral calculus could be discharged after conservative management. |
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