Sun Ke, Chen Shouyu, Zhao Jiabi, Wang Bin, Yang Yang, Wang Yin, Wu Chunyan, Sun Xiwen
Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Front Oncol. 2021 Dec 21;11:792062. doi: 10.3389/fonc.2021.792062. eCollection 2021.
To establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule (SPN) or mass (SPM) on computed tomography (CT).
A total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n=366), validation (n=46), and test (n=47) sets. Histopathologic analysis was available for each patient. An end-to-end CNN model was proposed to predict the natural history of solid indeterminate SPN/SPMs on CT. Receiver operating characteristic curves were plotted to evaluate the predictive performance of the proposed CNN model. The accuracy, sensitivity, and specificity of diagnoses by radiologists alone were compared with those of diagnoses by radiologists by using the CNN model to assess its clinical utility.
For the CNN model, the AUC was 91% (95% confidence interval [CI]: 0.83-0.99) in the test set. The diagnostic accuracy of radiologists with the CNN model was significantly higher than that without the model (89 66%, P<0.01; 87 61%, P<0.01; 85 66%, P=0.03, in the train, validation, and test sets, respectively). In addition, while there was a slight increase in sensitivity, the specificity improved significantly by an average of 42% (the corresponding improvements in the three sets ranged from 43, 33, and 42% to 82, 78, and 84%, respectively; P<0.01 for all).
The CNN model could be a valuable tool in non-invasively differentiating benign from malignant lesions manifesting as solid, indeterminate SPNs/SPMs on CT.
建立一种基于卷积神经网络（CNN）的非侵入性诊断模型，以区分在计算机断层扫描（CT）上表现为实性、不确定的孤立性肺结节（SPN）或肿块（SPM）的良性和恶性病变。
本回顾性研究最终纳入了459例CT上表现为实性、不确定SPN/SPM的患者，并将其分为训练组（n = 366）、验证组（n = 46）和测试组（n = 47）。每位患者均有组织病理学分析结果。提出了一种端到端的CNN模型来预测CT上实性、不确定SPN/SPM的自然病程。绘制受试者操作特征曲线以评估所提出的CNN模型的预测性能。将放射科医生单独诊断的准确性、敏感性和特异性与使用CNN模型的放射科医生诊断的准确性、敏感性和特异性进行比较，以评估其临床实用性。
对于CNN模型，测试组的曲线下面积（AUC）为91%（95%置信区间[CI]：0.83 - 0.99）。使用CNN模型的放射科医生的诊断准确性显著高于不使用该模型的放射科医生（训练组、验证组和测试组的诊断准确性分别为89±66%，P<0.01；87±61%，P<0.01；85±66%，P = 0.03）。此外，虽然敏感性略有增加，但特异性显著提高，平均提高了42%（三组相应的提高范围分别从43%、33%和42%提高到82%、78%和84%；所有P<0.01）。
CNN模型可能是一种有价值的工具，可用于非侵入性地区分CT上表现为实性、不确定SPN/SPM的良性和恶性病变。
