Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, 610041 Chengdu, Sichuan, China.
Precision Medicine Research Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, China.
Front Biosci (Landmark Ed). 2022 Jul 4;27(7):212. doi: 10.31083/j.fbl2707212.
Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice.
This study recruited participants prospectively in two rural sites of western China. A deep learning system was developed to assist clinicians to identify the nodules and evaluate the malignancy with state-of-the-art performance assessed by recall, free-response receiver operating characteristic curve (FROC), accuracy (ACC), area under the receiver operating characteristic curve (AUC).
This study enrolled 12,360 participants scanned by mobile CT vehicle, and detected 9511 (76.95%) patients with pulmonary nodules. Majority of participants were female (8169, 66.09%), and never-smokers (9784, 79.16%). After 1-year follow-up, 86 patients were diagnosed with lung cancer, with 80 (93.03%) of adenocarcinoma, and 73 (84.88%) at stage I. This deep learning system was developed to detect nodules (recall of 0.9507; FROC of 0.6470) and stratify the risk (ACC of 0.8696; macro-AUC of 0.8516) automatically.
A novel model for lung cancer screening, the integration mobile CT with deep learning, was proposed. It enabled specialists to increase the accuracy and consistency of workflow and has potential to assist clinicians in detecting early-stage lung cancer effectively.
肺癌筛查面临的现有挑战包括 CT 扫描仪不可及和读者间的变异性，尤其是在资源有限的地区。移动 CT 与深度学习技术的结合激发了常规临床实践中的创新。
本研究前瞻性地在中囯西部的两个农村点招募参与者。开发了一种深度学习系统，以协助临床医生识别结节并评估恶性肿瘤，其性能由召回率、自由反应接收者操作特征曲线（FROC）、准确性（ACC）和接收者操作特征曲线下的面积（AUC）来评估。
本研究共纳入 12360 名接受移动 CT 车扫描的参与者，检测出 9511 名（76.95%）患有肺结节的患者。大多数参与者为女性（8169 名，66.09%）和不吸烟者（9784 名，79.16%）。经过 1 年的随访，86 名患者被诊断为肺癌，其中 80 名（93.03%）为腺癌，73 名（84.88%）为 I 期。本研究开发了一种用于自动检测结节（召回率为 0.9507；FROC 为 0.6470）和分层风险（ACC 为 0.8696；宏观-AUC 为 0.8516）的深度学习系统。
提出了一种新的肺癌筛查模型，将移动 CT 与深度学习相结合，使专家能够提高工作流程的准确性和一致性，并有可能协助临床医生有效检测早期肺癌。
