Smoking-Related Comorbidities Detected Through Low-dose CT Lung Cancer Screening: Current Evidence and Future Directions.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Cochin Hospital, Université de Paris Cité, Paris, France. Electronic address: [email protected].
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Republic of Korea.
- Dept of Radiology, University Medical Center Groningen/University of Groningen, Groningen, the Netherlands.
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
- Department of Radiology, Antwerp University Hospital, Edegem, Belgium.
Abstract
Low-dose computed tomography (LDCT) has been established in the past decade as an important and effective tool for lung cancer screening (LCS) in high-risk individuals, with large trials demonstrating significant lung cancer mortality reduction. Beyond pulmonary nodules, LDCT frequently detects a range of smoking-related additional findings, including emphysema, coronary artery calcium, interstitial lung abnormalities (ILA), osteoporosis, and sarcopenia. Accordingly, here we review current evidence on the prevalence, prognostic value, and clinical implications of such smoking-related findings in LCS, with a focus on the findings themselves, their relevance in COPD patients, emerging technologies and future directions for integration into screening protocols. Smoking-related comorbidities detected on LDCT are highly prevalent and independently associated with increased morbidity and mortality. AI-based tools are advancing the automated detection and quantification of smoking-related additional findings, offering potential for standardized, structured reporting. However, variability in guidelines, uncertain clinical benefit, and concerns about overdiagnosis and resource use present ongoing challenges. Integrating the additional smoking-related findings into LCS protocols will help to enhance the preventive value of screening. Future research should focus on defining actionable thresholds, assessing clinical impact, and developing harmonized guidelines. AI-driven automation will play a key role in enabling consistent, scalable reporting across screening programs.