Xu Hua, Yu Yuanyuan, Chang Jie, Hu Xifeng, Tian Zitong, Li Ouwen
Department of Infection Control, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, Jinan, China.
Data Science Institute, Shandong University, Jinan, Shandong, China.
Front Oncol. 2024 Aug 19;14:1424546. doi: 10.3389/fonc.2024.1424546. eCollection 2024.
The research aims to develop an advanced and precise lung cancer screening model based on Convolutional Neural Networks (CNN).
Based on the health medical big data platform of Shandong University, we developed a VGG16-Based CNN lung cancer screening model. This model was trained using the Computed Tomography scans data of patients from Pingyi Traditional Chinese Medicine Hospital in Shandong Province, from January to February 2023. Data augmentation techniques, including random resizing, cropping, horizontal flipping, color jitter, random rotation and normalization, were applied to improve model generalization. We used five-fold cross-validation to robustly assess performance. The model was fine-tuned with an SGD optimizer (learning rate 0.001, momentum 0.9, and L2 regularization) and a learning rate scheduler. Dropout layers were added to prevent the model from relying too heavily on specific neurons, enhancing its ability to generalize. Early stopping was implemented when validation loss did not decrease over 10 epochs. In addition, we evaluated the model's performance with Area Under the Curve (AUC), Classification accuracy, Positive Predictive Value (PPV), and Negative Predictive Value (NPV), Sensitivity, Specificity and F1 score. External validation used an independent dataset from the same hospital, covering January to February 2022.
The training and validation loss and accuracy over iterations show that both accuracy metrics peak at over 0.9 by iteration 15, prompting early stopping to prevent overfitting. Based on five-fold cross-validation, the ROC curves for the VGG16-Based CNN model, demonstrate an AUC of 0.963 ± 0.004, highlighting its excellent diagnostic capability. Confusion matrices provide average metrics with a classification accuracy of 0.917 ± 0.004, PPV of 0.868 ± 0.015, NPV of 0.931 ± 0.003, Sensitivity of 0.776 ± 0.01, Specificity of 0.962 ± 0.005 and F1 score of 0.819 ± 0.008, respectively. External validation confirmed the model's robustness across different patient populations and imaging conditions.
The VGG16-Based CNN lung screening model constructed in this study can effectively identify lung tumors, demonstrating reliability and effectiveness in real-world medical settings, and providing strong theoretical and empirical support for its use in lung cancer screening.
本研究旨在开发一种基于卷积神经网络（CNN）的先进且精确的肺癌筛查模型。
基于山东大学健康医疗大数据平台，我们开发了一种基于VGG16的CNN肺癌筛查模型。该模型使用山东省平邑县中医医院2023年1月至2月患者的计算机断层扫描数据进行训练。应用了数据增强技术，包括随机调整大小、裁剪、水平翻转、颜色抖动、随机旋转和归一化，以提高模型的泛化能力。我们使用五折交叉验证来稳健地评估性能。该模型使用SGD优化器（学习率0.001、动量0.9和L2正则化）和学习率调度器进行微调。添加了随机失活层以防止模型过度依赖特定神经元，增强其泛化能力。当验证损失在10个轮次内没有下降时实施提前停止。此外，我们使用曲线下面积（AUC）、分类准确率、阳性预测值（PPV）、阴性预测值（NPV）、灵敏度、特异性和F1分数评估了模型的性能。外部验证使用了同一家医院2022年1月至2月的独立数据集。
迭代过程中的训练和验证损失及准确率表明，两个准确率指标在第15次迭代时均达到0.9以上的峰值，促使提前停止以防止过拟合。基于五折交叉验证，基于VGG16的CNN模型的ROC曲线显示AUC为0.963±0.004，突出了其出色的诊断能力。混淆矩阵提供的平均指标显示分类准确率为0.917±0.004，PPV为0.868±0.015，NPV为0.931±0.003，灵敏度为0.776±0.01，特异性为0.962±0.005，F1分数为0.819±0.008。外部验证证实了该模型在不同患者群体和成像条件下的稳健性。
本研究构建的基于VGG16的CNN肺癌筛查模型能够有效识别肺部肿瘤，在实际医疗环境中显示出可靠性和有效性，为其在肺癌筛查中的应用提供了有力的理论和实证支持。
