Evidence map›Paper›PMID 39830756›Full record

ArticleTranslational lung cancer research2024

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

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

Abstract read
In one paragraph

Article in Translational lung cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Hyun Joo Shin *Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
Se Hyun Kwak *Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
Kyeong Yeon KimDivision of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
Na Young KimDepartment of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Kyungsun NamDepartment of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Young Jin KimDepartment of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Eun-Kyung KimDepartment of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
Young Joo Suh *Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-2078-5832
Eun Hye Lee *Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.ORCID https://orcid.org/0000-0003-2570-3442

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencedetectionLung cancerradiography

Identifiers

PMID39830756
PMCPMC11736575

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.