Se Hyun Kwak, Eun-Kyung Kim, Myung Hyun Kim, Eun Hye Lee, Hyun Joo Shin
Abstract
Purpose
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.
Introduction
Lung cancer is the second most commonly diagnosed cancer, with an estimated 2.2 million new cancer cases and 1.8 million deaths worldwide in 2020 [1]. It is also the leading cause of morbidity and mortality from cancer in both men and women [2]. Although the 5-year relative survival rate for lung cancer in South Korea increased from 16.6% in 2001–2005 to 34.7% in 2013–2019 [3], it still remains significantly lower than that of other cancers. This is probably because most lung cancer patients are diagnosed at an advanced stage as there are no obvious specific symptoms in the early stage of the disease [4]. The clinical stage at presentation has the greatest impact on the prognosis of lung cancer, which indicates that early detection and screening methods are of paramount importance [5].
Materials and methods
Participants
The Institutional Review Board of Yongin Severance Hospital approved this retrospective study, and the requirement for informed consent was waived (IRB No. 2021-0491-001). Patients with pathologically confirmed lung cancer after surgical resection from March 2020 to February 2022 were retrospectively reviewed. Among them, the patients with incidentally detected resectable lung cancer were included in this study. We defined the incidentally detected resectable lung cancer as follows; (1) patients who had visited outpatient clinic except for pulmonology or thoracic surgery department for the evaluation or treatment of extrapulmonary diseases, (2) discovered lung nodule on initial chest radiographs by AI and confirmed it as lung cancer after the resection. We excluded patients who had initially visited the pulmonology or thoracic surgery department, were referred from another hospital owing to the suspicion of a lung abnormality in chest imaging, visited another department owing to respiratory symptoms, or underwent a health check-up or screening for lung cancer.
We retrospectively reviewed the medical records of the patients to describe the entire process of detecting lung cancer by using AI in chest radiographs. In addition to the age and sex of the included patients, information regarding the final pathologic diagnosis, tumor location, size, characteristics, TNM stage, and stage, which was based on the AJCC 8th edition, were evaluated using the pathologic reports after surgical resection.
Results
From March 2020 to February 2022, 75 patients were confirmed to have lung cancer on pathology following lung resection in our hospital. Among them, 13 patients (17.3%, male:female = 9:3, median = 65 years, range, 52–79 years) had incidentally detected resectable lung cancer. The remaining 62 patients were excluded because they initially visited the pulmonology or thoracic surgery department because of known lung abnormalities.
Discussion
Among all patients with resectable lung cancer, 13 patients (17.3%) had incidentally detected lung cancer, and all of them had first undergone chest radiographs. Regardless of the size and characteristics of the nodules, AI-based lesion detection software detected all of the lesions as nodules with a median abnormality score of 78%. Eight patients underwent chest radiographs for the evaluation of extrapulmonary diseases, while the remaining five underwent radiographs in preparation of an operation or procedure concerning other body parts and were not expected to have pulmonary problems when they first underwent the chest radiographs. Even though they were not expected to have lung lesions, 61.5% of patients had a prompt consultation with a pulmonologist on the same day as the chest radiograph examination and before receiving the official radiologists’ report.
Conclusion
This study examined the actual cases of unexpectedly detected early lung cancer, which were all detected by AI-based lesion detection software as nodules. About 62% of the patients had a consultation with a pulmonologist for their abnormal chest radiograph findings on the same day when the radiograph was taken and before receiving the radiologists’ official report. With the increasing use of AI in medical imaging, the actual benefit of AI in chest radiographs for the detection of unexpected early lung cancer needs to be validated.
Acknowledgments
The authors thank Medical Illustration & Design, part of the Medical Research Support Services of Yonsei University College of Medicine, for all artistic support related to this work.
Citation: Kwak SH, Kim E-K, Kim MH, Lee EH, Shin HJ (2023) Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs. PLoS ONE 18(3): e0281690. https://doi.org/10.1371/journal.pone.0281690
Editor: Jun Hyeok Lim, Inha University Hospital, REPUBLIC OF KOREA
Received: November 1, 2022; Accepted: January 29, 2023; Published: March 10, 2023
Copyright: © 2023 Kwak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by a faculty research grant of Yonsei University College of Medicine (6-2021-0227). In addition, this work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2022-00166711). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: AI, artificial intelligence; CT, computed tomography; PACS, picture archiving and communicating system
