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| 三维ResNet 网络预测肺腺癌结节亚型的效能及其稳定性 | |
| 引用本文： | 骆源1,徐启飞2,吕泽政1,蔡娜1,郭丽1. 三维ResNet 网络预测肺腺癌结节亚型的效能及其稳定性[J]. 天津医科大学学报, 2022, 0(3): 295-300 |
| 作者姓名： | 骆源1 徐启飞2 吕泽政1 蔡娜1 郭丽1 |
| 作者单位： | （1.天津医科大学医学技术学院医学图像处理教研室，天津300203；2.山东省临沂市人民医院影像科，临沂276000） |
| 摘 要： | 目的:探究ResNet模型对肺腺癌不同亚型结节的分类表现及稳定性。方法:回顾性收集2014 年2 月—2020 年10 月期间的364 例肺腺癌结节CT 影像数据，以7∶3 的比例分为训练集和内部测试集，将2020 年4 月到2020 年11 月的58 例结节数据作为外部测试集。使用基于ResNet的三维卷积神经网络在训练集中进行训练以及调参，并使用内部测试集和外部测试集对模型的准确性及泛化性进行评估。使用随机中心移动和掩膜处理的方式分别以内部测试集和外部测试集为基础构造新的测试集，新数据集对模型进行测试验证模型的稳定性。结果:模型在内部测试集AUC 为0.949 1（95%CI:0.910 8～0.987 4），模型在随机中心移动以及掩膜处理之后的数据集的AUC 值分别为0.940 4 和0.918 1， 与其差异无统计学意义（P 值分别为0.425 3 和0.239 3）。在外部测试集中模型AUC 为0.959 6（95%CI:0.901 2～1.000 0），在用于稳定性测试的随机中心移动以及掩膜处理之后的数据集中，模型所得AUC 分别为0.948 5和0.947 3，与其同样差异无统计学意义（均P>0.05）。结论:ResNet 模型对肺腺癌结节亚型有优异的鉴别能力，并且具有一定稳定性。 |
| 关 键 词： | 人工智能 深度学习 卷积神经网络 肺腺癌 诊断 |
Efficacy and stability of 3D ResNet for predicting nodule subtypes in lung adenocarcinoma |
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| LUO Yuan1,XU Qi-fei2,LYU Ze-zheng1,CAI Na1,GUO Li1. Efficacy and stability of 3D ResNet for predicting nodule subtypes in lung adenocarcinoma[J]. Journal of Tianjin Medical University, 2022, 0(3): 295-300 | |
| Authors: | LUO Yuan1 XU Qi-fei2 LYU Ze-zheng1 CAI Na1 GUO Li1 |
| Affiliation: | （1．Department of Medical Image Processing，School of Medical Technology，Tianjin Medical University，Tianjin 300203，China；2 .Department of Imaging，Linyi People′s Hospital，Linyi 276000，China） |
| Abstract: | Objective:To investigate the classification performance and model stability of ResNet models for different subtypes of nodulesin lung adenocarcinoma. Methods: The CT image of 364 lung adenocarcinoma nodules collected retrospectively between February 2014and October 2020 were divided into a training set and an internal test set in a ratio of 7∶3，and data of 58 nodules from April 2020 to November2020 were used as the external test set. The ResNet-based 3D convolutional neural network was trained and tuned in the training set，and the accuracy and generalization of themodel was evaluated using both internal and external test sets. To verify the stability of the model，two new test sets were constructed using random center shifts and masking process in both internal and external test set，and the modelwas tested using the new test set. Results:The model obtained an AUC of 0.949 1（95% CI:0.910 8-0.987 4）on the internal test set，andthe AUC values of the model were not statistically different（P=0.425 3 and 0.239 3，respectively）from those measured on the data setswith the random center shift and after the masking process. The model AUC in the external test set was 0.959 6（95% CI :0.901 2-1.000 0）.In the dataset after the random center shift and mask processing used for stability testing，the AUC obtained for the model（0.948 5 and0.947 3，respectively）was again not statistically different from it（all P>0.05）. Conclusion:ResNet model has excellent ability todiscriminate subtypes of lung adenocarcinoma，and the model has considerable stability. |
| Keywords: | artificial intelligence deep learning convolutional neural network lung adenocarcinoma diagnosis |
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