Effectiveness and implementation challenges of mobile low-dose computed tomography units for early lung cancer detection: a systematic review.

A mobile low-dose computed tomography (LDCT) unit is an effective strategy for early lung cancer detection, particularly in high-risk populations affected by social and healthcare disparities. However, existing reviews are limited by only focusing on diagnostic outcomes. This study aimed to evaluate the characteristics, effectiveness, and key influencing factors of mobile LDCT-based unit interventions. A literature search was conducted from the inception of each bibliographic database to March 1, 2025. Two independent reviewers performed the study selection, quality assessment, and data extraction. Methodological quality was assessed via the Cochrane Risk of Bias 2 tool for randomized studies and the ROBINS-I tool for non-randomized studies. A qualitative synthesis was used to analyze intervention characteristics and effectiveness. Influencing factors were synthesized via the Consolidated Framework for Implementation Research (CFIR). Eighteen studies were included. Suspected cases ranged 23-847 (detection 1.06-16.37%), with confirmed cases 2-107 (0.33-4.33%). Five studies demonstrated economic efficiency for the mobile LDCT-based unit, with early detection yielding favorable cost ratios (¥11,143-16,950 per case) and substantial savings over late-stage treatment. Financial analyses indicated positive returns (e.g., net present value = $1 M; internal rate of return = 34.6%). Screen-detected cases reduced treatment costs by 56% and reduced hospital stays by 70%. Influencing factors, categorized via the CFIR, included intervention characteristics (n = 2), outer setting (n = 4), inner setting (n = 4), and characteristics of individuals (n = 2) but excluded implementation process (n = 0). This review shows mobile LDCT-based units have the potential benefit for early lung cancer detection and economic efficiency, particularly for underserved populations by reducing access barriers. Future research should identify implementation factors and develop practical frameworks to optimize program delivery and impact. https://www.crd.york.ac.uk/PROSPERO/view/CRD420251026810, Identifier CRD420251026810.