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Opportunities for Predicting Lung Cancer Screening Nonadherence: A Systematic Review and Meta-Analysis.

Yannan Lin¹, Ruiwen Ding¹, Drew Moghanaki²

¹Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.

Journal of the American College of Radiology : JACR

|December 3, 2025

Summary

This summary is machine-generated.

Machine learning shows promise for predicting lung cancer screening nonadherence. Further development using large national databases is needed to create tailored interventions and improve patient outcomes.

Keywords:

Lung cancer screening meta-analysis nonadherence risk assessment systematic review

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Area of Science:

Medical Informatics
Public Health
Oncology

Background:

Low adherence to lung cancer screening (LCS) limits its effectiveness in reducing mortality.
Individualized risk prediction for nonadherence is crucial for designing targeted interventions.
Machine learning (ML) offers potential for predicting LCS nonadherence risk.

Purpose of the Study:

To systematically review and meta-analyze the literature on ML models for predicting LCS nonadherence risk.
To assess the current state of ML in identifying individuals at high risk of LCS nonadherence.

Main Methods:

Systematic literature search of PubMed, Embase, and Web of Science (April 2014 - May 2025).
Extraction of study characteristics, nonadherence data, and prediction model performance.
Meta-analysis of prediction model performance (area under the receiver operating characteristics curve).

Main Results:

Nine studies (2020-2025) with varying sample sizes (168-28,294) were included.
Pooled cross-validated AUC was 0.80 (95% CI, 0.64-0.90) across three populations, with high heterogeneity.
Explored feasibility of using national databases for future ML model development.

Conclusions:

Current ML models for predicting LCS nonadherence are underdeveloped.
Large, multicenter, national databases are essential for developing robust prediction models.
Investment is needed to create models that identify patients requiring tailored adherence interventions.