目的 探讨基于深度学习的双源计算机断层扫描(DSCT)影像学模型对鉴别肺结节良恶性的价值。方法 回顾性分析2023-01至2025-01医院收治的140例肺结节患者临床资料,将DSCT影像标注出的215个肺结节作为研究对象,根据肺结节的良、恶性分为良性组(n=124)和恶性组(n=91)。另按照训练集与验证集7:3的比例,选取2025-02至2026-01收治的60例肺结节患者,将DSCT影像标注出的96个肺结节作为验证集。对肺结节患者的胸部DSCT图像,采用基于PyTorch的深度学习框架,构建3种卷积神经网络模型(ResNet50、DenseNet121、ShuffleNetV2),所有模型均采用在ImageNet数据集上预训练的权重进行迁移学习。通过受试者工作特征(ROC)曲线下面积(AUC)、敏感度、特异度、阳性预测值、约登指数综合评估模型的诊断效能,并在验证集中进行验证。在验证集中,采用Delong检验比较最优深度学习模型与3位不同年资的放射科医师的诊断效能。结果 训练集与验证集的肺结节恶性率分别为42.33%、41.67%。DenseNet121模型在训练集及验证集的AUC(0.940,95%CI=0.910~0.969;0.909,95%CI=0.847~0.971)均优于其他模型。DenseNet121模型的敏感度(81.30%;90.00%)、特异度(90.30%;76.80%)、阳性预测值(86.80%;91.50%)及约登指数(0.716;0.668)均较优,且具有良好的校准度和临床获益。DenseNet121模型的AUC显著高于低年资医师及中年资医师的AUC,差异均有统计学意义(Z=2.637,P=0.008;Z=2.130,P=0.033);DenseNet121模型与高年资医师的AUC差异无统计学意义(Z=1.296,P=0.195)。结论 基于深度学习的DSCT影像学模型在鉴别肺结节良恶性方面具有较高的诊断价值,有望成为辅助中低年资放射科医师诊断的有效工具。
Abstract
Objective To investigate the value of a deep learning-based dual-source computed tomography (DSCT) imaging model in differentiating benign and malignant pulmonary nodules. Methods The clinical data of 140 patients with pulmonary nodules admitted to a hospital from January 2023 to January 2025 were retrospectively analyzed. A total of 215 pulmonary nodules annotated on DSCT images were included as the study subjects and divided into a benign group (n=124) and a malignant group (n=91) based on the nature of the nodules. Another 60 patients with pulmonary nodules admitted to the same hospital from February 2025 to January 2026 were selected as the validation set, with 96 pulmonary nodules annotated on DSCT images. The chest DSCT images of the patients with pulmonary nodules were analyzed , and three convolutional neural network models (ResNet50, DenseNet121, ShuffleNetV2) were constructed using a PyTorch-based deep learning framework. All models employed the weights pre-trained on the ImageNet dataset for transfer learning. The diagnostic efficacy of the models was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive predictive value, and Youden index, and validated in the validation set. In the validation set, the Delong test was used to compare the diagnostic performance of the optimal deep learning model with that of three radiologists of different seniority. Results The malignancy rates of pulmonary nodules in the training set and validation set were 42.33% and 41.67%, respectively. The DenseNet121 model demonstrated superior AUC values in the training set and validation set (0.940, 95% CI=0.910-0.969; 0.909, 95%CI=0.847-0.971) compared to other models. The DenseNet121 model also showed favorable sensitivity (81.30%; 90.00%), specificity (90.30%; 76.80%), positive predictive value (86.80%; 91.50%), and Youden index (0.716; 0.668), along with good calibration and clinical benefits. The AUC of the DenseNet121 model was significantly higher than that of junior and intermediate-seniority radiologists (Z=2.637, P=0.008; Z=2.130, P=0.033). However, there was no statistically significant difference in AUC between the DenseNet121 model and the senior radiologists (Z=1.296, P=0.195). Conclusions The deep learning-based DSCT imaging model has high diagnostic value in differentiating benign and malignant pulmonary nodules and is expected to be an effective tool to assist junior and intermediate-seniority radiologists in diagnosis.
关键词
深度学习 /
双源计算机断层扫描 /
肺结节 /
良恶性鉴别 /
诊断价值
Key words
deep learning /
dual-source computed tomography /
pulmonary nodules /
benign-malignant differentiation /
diagnostic value
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基金
厦门市医疗卫生指导性项目(3502Z20224ZD1240)
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