Cheo Hao Min, Ong Chern Yue Glen, Ting Yonghan
National University Hospital, Singapore 119074, Singapore.
Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore.
Healthcare (Basel). 2025 Jun 24;13(13):1510. doi: 10.3390/healthcare13131510.
The introduction of lung cancer screening (LCS) programmes will lead to a surge in imaging volumes and place greater demands on radiologists to provide timely and accurate interpretation. This increased workload risks overburdening a limited radiologist workforce, delaying diagnosis, and worsening burnout. Advancements in artificial intelligence (AI) models offer the potential to detect and classify pulmonary nodules without a loss in diagnostic performance. A systematic review of AI performance in lung cancer detection on computed tomography (CT) scans was conducted. Multiple databases like Medline, Embase, PubMed, and Cochrane were searched within a 12-year range from 1 January 2010 to 21 December 2022. Fourteen studies were selected for this systematic review, with seven in the detection subgroup and eight in the classification subgroup. Compared to radiologists' performance in the respective articles, the AI models demonstrated a higher sensitivity (86.0-98.1% against 68-76%) but lower specificity (77.5-87% against 87-91.7%) for the detection of lung nodules. In classifying the malignancy of lung nodules, AI models generally showed a greater sensitivity (60.58-93.3% against 76.27-88.3%), specificity (64-95.93% against 61.67-84%), and accuracy (64.96-92.46% against 73.31-85.57%) over radiologists. AI models for the detection and classification of pulmonary lesions on CT have the potential to augment CT thorax interpretation while maintaining diagnostic accuracy and could potentially be harnessed to overcome challenges in the implementation of lung cancer screening programmes.
肺癌筛查（LCS）项目的引入将导致影像检查量激增，对放射科医生及时、准确解读影像的要求也更高。工作量的增加可能使有限的放射科医生队伍不堪重负，导致诊断延误，并加剧职业倦怠。人工智能（AI）模型的进步为检测和分类肺结节提供了可能，且不会降低诊断性能。我们对人工智能在计算机断层扫描（CT）肺癌检测中的性能进行了系统评价。在2010年1月1日至2022年12月21日的12年范围内，检索了多个数据库，如Medline、Embase、PubMed和Cochrane。本系统评价共纳入14项研究，其中检测亚组7项，分类亚组8项。与各文章中放射科医生的表现相比，人工智能模型在检测肺结节方面显示出更高的敏感性（86.0 - 98.1%对68 - 76%），但特异性较低（77.5 - 87%对87 - 91.7%）。在对肺结节的恶性程度进行分类时，人工智能模型总体上显示出比放射科医生更高的敏感性（60.58 - 93.3%对76.27 - 88.3%）、特异性（64 - 95.93%对61.67 - 84%）和准确性（64.96 - 92.46%对73.31 - 85.57%）。用于CT上肺病变检测和分类的人工智能模型有潜力在保持诊断准确性的同时增强胸部CT解读，并有可能用于克服肺癌筛查项目实施中的挑战。
