Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs

• Published in: PloS one Vol. 18; no. 3; p. e0281690
• Main Authors: Kwak, Se Hyun, Kim, Eun-Kyung, Kim, Myung Hyun, Lee, Eun Hye, Shin, Hyun Joo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.03.2023; Public Library of Science (PLoS)
• Subjects: Artificial Intelligence; Body parts; Cardiovascular disease; Chest; Clinical medicine; Computer and Information Sciences; Diagnosis; Engineering and Technology; Health surveillance; Hospitals; Humans; Lesions; Lung cancer; Lung diseases; Lung Neoplasms - pathology; Medical imaging; Medical prognosis; Medical records; Medical screening; Medicine and Health Sciences; Methods; Mortality; Nodules; Patients; People and Places; Pneumothorax; Pulmonology; Radiographs; Radiography; Radiography, Medical; Radiography, Thoracic - methods; Retrospective Studies; Software; Thoracic surgery; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0281690

Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer. Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because commercially available AI-based lesion detection software was integrated for all chest radiographs in our hospital, we reviewed the clinical process of detecting lung cancer using AI in chest radiographs. Among the 75 patients with pathologically proven resectable lung cancer, 13 (17.3%) had incidentally discovered lung cancer with a median size of 2.6 cm. Eight patients underwent chest radiograph for the evaluation of extrapulmonary diseases, while five underwent radiograph in preparation of an operation or procedure concerning other body parts. All lesions were detected as nodules by the AI-based software, and the median abnormality score for the nodules was 78%. Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report. Total and invasive sizes of the part-solid nodules were 2.3-3.3 cm and 0.75-2.2 cm, respectively. This study demonstrates actual cases of unexpectedly detected resectable early lung cancer using AI-based lesion detection software. Our results suggest that AI is beneficial for incidental detection of early lung cancer in chest radiographs.