人工智能在肺癌筛查中的应用:一项针对中国体检人群的真实世界研究。

IF 2.3 3区 医学 Q3 ONCOLOGY
Thoracic Cancer Pub Date : 2024-10-01 Epub Date: 2024-08-29 DOI:10.1111/1759-7714.15428
Jiaxuan Wu, Ruicen Li, Jiadi Gan, Qian Zheng, Guoqing Wang, Wenjuan Tao, Ming Yang, Wenyu Li, Guiyi Ji, Weimin Li
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引用次数: 0

摘要

背景:随着胸部计算机断层扫描(CT)图像的迅速增加,放射科医生面临的工作量也急剧增加。不可否认,在临床治疗中使用人工智能(AI)图像辅助诊断系统是医学发展的一大趋势。因此,为了探讨目前人工智能系统在临床应用中的价值和诊断准确性,我们旨在比较人工智能系统和医生对肺部良恶性结节的检测和鉴别,从而为临床应用提供理论依据:研究对象为在华西医院健康管理中心接受胸部低剂量螺旋CT肺癌筛查的23 336例患者。我们对人工智能辅助阅片和人工判读进行了对比分析,重点关注良性和恶性肺结节的检测和鉴别:结果:与人工判读相比,人工智能辅助判读的筛查阳性率和诊断出肺部恶性结节的概率均明显高于人工判读(p 结论:人工智能辅助判读的筛查阳性率和诊断出肺部恶性结节的概率均明显高于人工判读:我们的研究结果表明,与人工判读相比,人工智能辅助判读的筛查阳性率和肺癌检出率更高。因此,在临床实践中,人工智能作为肺癌筛查的辅助工具具有相当大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.

Background: With the rapid increase of chest computed tomography (CT) images, the workload faced by radiologists has increased dramatically. It is undeniable that the use of artificial intelligence (AI) image-assisted diagnosis system in clinical treatment is a major trend in medical development. Therefore, in order to explore the value and diagnostic accuracy of the current AI system in clinical application, we aim to compare the detection and differentiation of benign and malignant pulmonary nodules between AI system and physicians, so as to provide a theoretical basis for clinical application.

Methods: Our study encompassed a cohort of 23 336 patients who underwent chest low-dose spiral CT screening for lung cancer at the Health Management Center of West China Hospital. We conducted a comparative analysis between AI-assisted reading and manual interpretation, focusing on the detection and differentiation of benign and malignant pulmonary nodules.

Results: The AI-assisted reading exhibited a significantly higher screening positive rate and probability of diagnosing malignant pulmonary nodules compared with manual interpretation (p < 0.001). Moreover, AI scanning demonstrated a markedly superior detection rate of malignant pulmonary nodules compared with manual scanning (97.2% vs. 86.4%, p < 0.001). Additionally, the lung cancer detection rate was substantially higher in the AI reading group compared with the manual reading group (98.9% vs. 90.3%, p < 0.001).

Conclusions: Our findings underscore the superior screening positive rate and lung cancer detection rate achieved through AI-assisted reading compared with manual interpretation. Thus, AI exhibits considerable potential as an adjunctive tool in lung cancer screening within clinical practice settings.

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来源期刊
Thoracic Cancer
Thoracic Cancer ONCOLOGY-RESPIRATORY SYSTEM
CiteScore
5.20
自引率
3.40%
发文量
439
审稿时长
2 months
期刊介绍: Thoracic Cancer aims to facilitate international collaboration and exchange of comprehensive and cutting-edge information on basic, translational, and applied clinical research in lung cancer, esophageal cancer, mediastinal cancer, breast cancer and other thoracic malignancies. Prevention, treatment and research relevant to Asia-Pacific is a focus area, but submissions from all regions are welcomed. The editors encourage contributions relevant to prevention, general thoracic surgery, medical oncology, radiology, radiation medicine, pathology, basic cancer research, as well as epidemiological and translational studies in thoracic cancer. Thoracic Cancer is the official publication of the Chinese Society of Lung Cancer, International Chinese Society of Thoracic Surgery and is endorsed by the Korean Association for the Study of Lung Cancer and the Hong Kong Cancer Therapy Society. The Journal publishes a range of article types including: Editorials, Invited Reviews, Mini Reviews, Original Articles, Clinical Guidelines, Technological Notes, Imaging in thoracic cancer, Meeting Reports, Case Reports, Letters to the Editor, Commentaries, and Brief Reports.
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