| 韩静,张乐星,何林阳,冯长锋,郗玉珍,丁忠祥,许阳阳,沈起钧.3D Res2Net深度学习模型预测肺实性结节体积倍增时间[J].中国医学影像技术,2024,40(10):1514~1518 |
| 3D Res2Net深度学习模型预测肺实性结节体积倍增时间 |
| 3D Res2Net deep learning model for predicting volume doubling time of solid pulmonary nodule |
| 投稿时间：2024-04-30 修订日期：2024-05-27 |
| DOI：10.13929/j.issn.1003-3289.2024.10.012 |
| 中文关键词: 肺肿瘤 体层摄影术,X线计算机 深度学习 |
| 英文关键词:lung neoplasms tomography,X-ray computed deep learning |
| 基金项目:浙江省"尖兵"研发攻关计划(2022C03046)、浙江省基础公益研究计划项目(LTGY24H010001)、浙江省医药卫生科技计划项目(2023KY953)。 |
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| 中文摘要: |
| 目的 观察3D Res2Net深度学习模型预测肺实性结节体积倍增时间(VDT)的价值。方法 回顾性分析734例肺实性结节患者胸部CT资料,根据随访期间肺结节体积增加是否≥25%将其分为进展组(n=218)与非进展组(n=516),并按7:3比例划分训练集(n=515)与验证集(n=219);利用多因素logistic回归分析基于组间差异有统计学意义的临床参数构建临床模型,采用卷积神经网络提取肺结节二维CT图像特征构建CT特征模型,基于Res2Net网络输入三维CT图像构建3D Res2Net模型;绘制受试者工作特征曲线,计算曲线下面积(AUC),以实际VDT为标准,评估临床模型、CT特征模型及3D Res2Net模型预测肺实性结节VDT≤400天的效能。结果 临床模型、CT特征模型及3D Res2Net模型预测效能差异均无统计学意义(P均＞0.05),其在训练集的AUC分别为0.689、0.698及0.734,在验证集分别为0.692、0.714及0.721。3D Res2Net模型预测肺实性结节VDT用时5~7 s、平均(5.92±1.08)s。结论 3D Res2Net模型可用于预测肺实性结节VDT以大幅缩短人工阅片时间。 |
| 英文摘要: |
| Objective To observe the value of 3D Res2Net deep learning model for predicting volume doubling time (VDT) of solid pulmonary nodule. Methods Chest CT data of 734 patients with solid pulmonary nodules were retrospectively analyzed. The patients were divided into progressive group (n=218) and non-progressive group (n=516) according to whether lung nodule volume increased by ≥25% during follow-up or not, also assigned into training set (n=515) and validation set (n=219) at a ratio of 7:3. Then a clinical model was constructed based on clinical factors being significantly different between groups, CT features model was constructed based on features of nodules on 2D CT images using convolutional neural network, and 3D Res2Net model was constructed based on Res2Net network using 3D CT images as input. Receiver operating characteristic curve was drawn, and the area under the curve (AUC) was calculated. Taken actual VDT as gold standard, the efficacy of the above models for predicting solid pulmonary nodule’ VDT≤400 days were evaluated. Results No significant difference of predicting efficacy for solid pulmonary nodule’ VDT≤400 days was found among clinical model, CT feature model and 3D Res2Net model, the AUC of which was 0.689, 0.698 and 0.734 in training set, 0.692, 0.714 and 0.721 in validation set, respectively. 3D Res2Net model needed 5—7 s to predict VDT of solid pulmonary nodules, with an average time of (5.92±1.08)s. Conclusion 3D Res2Net model could be used to predict VDT of solid pulmonary nodules, which might obviously reduce manual interpreting time. |
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