Smoking-Related Comorbidities Detected Through Low-Dose CT Imaging Lung Cancer Screening: Current Evidence and Future...

Area of Science:

• Pulmonary Medicine
• Radiology
• Oncology

Background:

• Low-dose computed tomography (LDCT) is a proven tool for lung cancer screening (LCS) in high-risk individuals, reducing mortality.
• LDCT frequently identifies additional smoking-related findings beyond pulmonary nodules, such as emphysema and coronary artery calcium.
• These incidental findings are prevalent and linked to increased morbidity and mortality.

Purpose of the Study:

• To review the evidence on the prevalence, prognostic value, and clinical implications of smoking-related findings detected during LCS.
• To explore the relevance of these findings in patients with chronic obstructive pulmonary disease (COPD).
• To discuss emerging technologies and future directions for integrating these findings into LCS protocols.

Main Methods:

• Review of current scientific literature and clinical trial data.
• Analysis of the prognostic significance of various smoking-related comorbidities identified on LDCT.
• Evaluation of the role of artificial intelligence (AI) in automated detection and reporting.

Main Results:

• LDCT detects numerous smoking-related comorbidities with significant prognostic value.
• These additional findings are highly prevalent in individuals undergoing LCS.
• AI tools show promise for standardized detection and quantification of these findings.

Conclusions:

• Integrating incidental smoking-related findings into LCS protocols can enhance preventive care.
• Further research is needed to establish actionable thresholds, assess clinical impact, and harmonize guidelines.
• AI-driven automation is crucial for scalable and consistent reporting in LCS programs.