Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China.
Thorac Cancer. 2022 Feb;13(4):602-612. doi: 10.1111/1759-7714.14305. Epub 2022 Jan 6.
Early identification of the malignant propensity of pulmonary ground-glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning-based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs.
This diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time-point CT scans. We developed a deep learning-based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the testing set. Afterwards, an observer study was conducted to compare the diagnostic performance between the deep learning model and two trained radiologists in the testing set. We further performed stratified analysis to further relieve the impact of histological types, nodule size, time interval between two CTs, and the component of GGNs. Receiver operating characteristic (ROC) analysis was used to assess the performance of all models.
The deep learning model that used integrated DL-features from initial and follow-up CT images yielded the best diagnostic performance, with an area under the curve of 0.841. The observer study showed that the accuracies for the deep learning model, junior radiologist, and senior radiologist were 77.17%, 66.89%, and 77.03%, respectively. Stratified analyses showed that the deep learning model and radiologists exhibited higher performance in the subgroup of nodule sizes larger than 10 mm. With a longer time interval between two CTs, the deep learning model yielded higher diagnostic accuracy, but no general rules were yielded for radiologists. Different densities of components did not affect the performance of the deep learning model. In contrast, the radiologists were affected by the nodule component.
Deep learning can achieve diagnostic performance on par with or better than radiologists in identifying pulmonary GGNs.
早期识别肺部磨玻璃结节（GGN）的恶性倾向可以减轻跟踪病变和个性化治疗适应的压力。本研究旨在开发一种基于深度学习的方法，使用连续 CT 成像来诊断肺部 GGN。
这项诊断研究回顾性纳入了 2009 年 7 月至 2019 年 3 月期间来自四川大学华西医院的 762 名 GGN 患者。所有患者均接受了手术切除，并至少进行了两次连续的 CT 扫描。我们使用来自 508 名患者的 1524 个 CT 切片的训练集，开发了一种基于深度学习的方法来识别 GGN，然后在测试集中评估了 256 名患者。之后，进行了观察者研究，以比较测试集中深度学习模型和两名训练有素的放射科医生的诊断性能。我们进一步进行了分层分析，以进一步减轻组织学类型、结节大小、两次 CT 之间的时间间隔以及 GGN 成分的影响。使用受试者工作特征（ROC）分析评估所有模型的性能。
使用初始和随访 CT 图像的集成深度学习特征的深度学习模型产生了最佳的诊断性能，曲线下面积为 0.841。观察者研究表明，深度学习模型、初级放射科医生和高级放射科医生的准确率分别为 77.17%、66.89%和 77.03%。分层分析表明，在结节大小大于 10mm 的亚组中，深度学习模型和放射科医生表现出更高的性能。两次 CT 之间的时间间隔较长时，深度学习模型的诊断准确率更高，但放射科医生没有得出一般规律。不同密度的成分不影响深度学习模型的性能。相比之下，放射科医生受到结节成分的影响。
深度学习在识别肺部 GGN 方面可以达到或优于放射科医生的诊断性能。
