Effectiveness of artificial intelligence for detecting operable lung cancer on chest radiographs.

IF 4 2区 医学 Q2 ONCOLOGY
Translational lung cancer research Pub Date : 2024-12-31 Epub Date: 2024-12-27 DOI:10.21037/tlcr-24-745
Hyun Joo Shin, Se Hyun Kwak, Kyeong Yeon Kim, Na Young Kim, Kyungsun Nam, Young Jin Kim, Eun-Kyung Kim, Young Joo Suh, Eun Hye Lee
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Abstract

Background: Despite the importance of early diagnosis of lung cancer and wide availability of chest radiography, the detection of operable stage lung cancer on chest radiographs (CXRs) remains challenging. This study aimed to investigate the effectiveness of artificial intelligence (AI)-based CXR analysis for detecting operable lung cancers.

Methods: Patients who underwent lung cancer surgery at two referral hospitals between March 2020 and February 2021 were retrospectively included in this study. Preoperative CXRs of the patients were analyzed using commercial AI-based lesion detection software, and the results of lesion location and types obtained using the software were reviewed by radiologists and pulmonologists, with computed tomography (CT) as a reference standard for determining nodule characteristics. Factors influencing AI detection of lung cancer on CXR were assessed using logistic regression analysis.

Results: Among the 594 patients who underwent surgery for lung cancer (median age: 65 years, 51.3% male), the sensitivity of AI for detecting lung cancer on CXR was 57.7%, and it identified 86% of CXR-visible lung cancers. Detection rates of lung cancer by AI increased according to the disease stage: 42.5% for stage IA, 86.3% for stage IB, and 90.9% for stages II-III. The detection rate increased to over 60% from stage IA2 onwards when tumor size exceeded 1 cm. Regarding lesion type on CT, 8.3%, 46.8%, and 77.3% of non-solid, part-solid, and solid nodules, respectively, were detected by AI. Multivariable analysis showed that nodule location in the upper zone [odds ratio (OR) 2.78, P<0.001], peripheral region (OR 4.59, P<0.001), and solid lesion diameter (OR 1.20, P<0.001) were significantly associated with AI detection of lung cancer.

Conclusions: AI could be an effective tool for detecting operable lung cancer on CXRs, particularly when lesions are larger and located in the upper and peripheral regions.

人工智能在胸片上检测可手术肺癌的有效性。
背景:尽管早期诊断肺癌的重要性和胸部x线摄影的广泛应用,但在胸部x线摄影(CXRs)上发现可手术期肺癌仍然具有挑战性。本研究旨在探讨基于人工智能(AI)的CXR分析在检测可手术肺癌中的有效性。方法:回顾性纳入2020年3月至2021年2月期间在两家转诊医院接受肺癌手术的患者。术前使用商用的基于人工智能的病变检测软件对患者的cxr进行分析,并由放射科医生和肺科医生对软件获得的病变位置和类型的结果进行审查,并以计算机断层扫描(CT)作为确定结节特征的参考标准。采用logistic回归分析评估人工智能在CXR中检测肺癌的影响因素。结果:594例肺癌手术患者(中位年龄65岁,男性51.3%),AI在CXR上检测肺癌的敏感性为57.7%,识别出86%的CXR可见肺癌。AI对肺癌的检出率随疾病分期而增加:IA期为42.5%,IB期为86.3%,II-III期为90.9%。从IA2期开始,当肿瘤大小超过1 cm时,检出率增加到60%以上。CT上病变类型方面,人工智能对非实性结节、半实性结节和实性结节的检出率分别为8.3%、46.8%和77.3%。多变量分析显示结节位于上区[比值比(OR) 2.78, p]。结论:人工智能可作为cxr检测可手术肺癌的有效工具,特别是当病变较大且位于上区和外周区时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
2.50%
发文量
137
期刊介绍: Translational Lung Cancer Research(TLCR, Transl Lung Cancer Res, Print ISSN 2218-6751; Online ISSN 2226-4477) is an international, peer-reviewed, open-access journal, which was founded in March 2012. TLCR is indexed by PubMed/PubMed Central and the Chemical Abstracts Service (CAS) Databases. It is published quarterly the first year, and published bimonthly since February 2013. It provides practical up-to-date information on prevention, early detection, diagnosis, and treatment of lung cancer. Specific areas of its interest include, but not limited to, multimodality therapy, markers, imaging, tumor biology, pathology, chemoprevention, and technical advances related to lung cancer.
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