Huang Wenjun, Deng Heng, Li Zhaobin, Xiong Zhanda, Zhou Taohu, Ge Yanming, Zhang Jing, Jing Wenbin, Geng Yayuan, Wang Xiang, Tu Wenting, Dong Peng, Liu Shiyuan, Fan Li
Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China.
School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China.
Front Oncol. 2023 Aug 17;13:1255007. doi: 10.3389/fonc.2023.1255007. eCollection 2023.
To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics.
This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise.
The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%.
The baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.
基于深度学习和放射组学得出的全肺基线CT特征，开发并验证预测磨玻璃结节（GGN）良恶性的模型。
这项回顾性研究纳入了来自3家医院的385个经病理证实的GGN。我们将来自医院1的239个GGN用作训练集和内部验证集；将来自医院2和医院3的115个和31个GGN分别用作外部测试集1和2。另外，将来自医院3的32个随访超过5年的稳定GGN用作外部测试集3。我们评估了基线胸部CT上GGN的临床和形态学特征，并同时提取了全肺放射组学特征。此外，使用卷积神经网络进一步辅助并提取基线全肺CT图像特征。我们使用反向传播神经网络，基于用于训练的不同特征搭配构建了5个预测模型。使用受试者工作特征曲线下面积（AUC）比较这5个模型的预测性能。使用德龙检验两两比较模型之间AUC的差异。
整合了临床形态学特征、全肺放射组学特征和全肺图像特征（CMRI）的模型在5个模型中表现最佳，在内部验证集、外部测试集1和外部测试集2中实现了最高的AUC，分别为0.886（95%CI：0.841 - 0.921）、0.830（95%CI：0.749 - 0.893）和0.879（95%CI：0.712 - 0.968）。在上述三个数据集中，CMRI模型与其他模型之间的AUC差异具有统计学意义（均P < 0.05）。此外，CMRI模型在外部测试集3中的准确率为96.88%。
基线全肺CT特征可用于预测GGN的良恶性，有助于对GGN进行更精细的管理。
