Research on Risk Transfer Pathways for Lung Cancer Among Middle-Aged and Older Individuals Using Deep Reinforcement Learning: Retrospective Cohort Study
Clinical Snapshot
PICO Framework
| P — Population | Middle-aged and older individuals from Health and Retirement Study (n=12,543) and China Health and Retirement Longitudinal Study (n=16,442) |
| I — Intervention | Deep Q-network (DQN) reinforcement learning model for risk stratification and pathway simulation |
| C — Comparator | Standard risk assessment without reinforcement learning optimization |
| O — Outcomes | Lung cancer risk transfer pathways, risk stratification accuracy, simulated reduction in lung cancer incidence |
Bottom Line
This retrospective cohort study developed a deep reinforcement learning model to predict lung cancer risk and simulate risk reduction pathways in middle-aged and older adults. While the model demonstrated good discriminative performance (AUC 0.906-0.927) across large international cohorts, the clinical utility remains questionable. The reported simulated risk reductions of 68.2% and 56.9% appear implausibly large and lack real-world validation. Critical methodological details are missing, including specific risk factors, outcome definitions, and comparison to established prediction models like the PLCOm2012. The concept of 'risk transfer pathways' is poorly defined, making clinical implementation unclear. Australian clinicians should await more detailed methodology, external validation in local populations, and demonstration of actual clinical outcomes before considering adoption. Current evidence-based lung cancer screening using low-dose CT in high-risk populations remains the standard approach.
Key Findings
P Value: Not reported
Effect Size: 68.2% simulated reduction in high-risk group, 56.9% in medium-risk group
Primary Outcome: Model accuracy for lung cancer risk prediction and simulated risk reduction through pathway optimization
Nnt Or Sensitivity: AUC 0.906-0.927 suggests good discriminative ability
Confidence Interval: Accuracy: 0.917-0.949, AUC: 0.906-0.927
Clinical Application
Limited by requirement for complex computational infrastructure and unclear clinical workflow integration Would require validation in Australian population; unclear alignment with current Cancer Australia screening guidelines and PBS-funded interventions Middle-aged and older adults at risk for lung cancer, particularly those requiring risk stratification
Abstract
BACKGROUND: Nowadays, lung cancer has a significantly high incidence rate worldwide. The mortality rate of lung cancer continues to rise; it is more common in middle-aged and older individuals and poses a great threat to human health. OBJECTIVE: This study aimed to assess the lung cancer risk among middle-aged and older individuals in a timely manner and to establish an efficient pathway for the risk transfer. METHODS: We proposed a deep reinforcement learning model based on deep Q-network (DQN) to explore the risk transfer pathway for lung cancer among middle-aged and older individuals. Risk stratification of lung cancer occurrence was deduced through deep neural network. The DQN model was developed using the Health and Retirement Study cohort for model training and internal validation. We also used the China Health and Retirement Longitudinal Study cohort for model external validation. Transfer simulation of multiple pathways in different cycles was calculated in a stratified risk groups-leveraged DQN model. RESULTS: We developed and evaluated the DQN method for optimizing the risk transfer pathway among middle-aged and older individuals, with accuracy ranging from 0.917 (95% CI 0.896-0.928) to 0.949 (95% CI 0.909-0.961) and area under curve ranging from 0.906 (95% CI 0.887-0.933) to 0.927 (95% CI 0.893-0.938). External validation was conducted to assess the model's effectiveness and availability. A total of 8780 and 3763 samples from the Health and Retirement Study were used for model training and internal testing, respectively, and 16,442 samples from the China Health and Retirement Longitudinal Study were used for external validation. The results showed that DQN models illuminated the optimal risk transfer pathways for stratified risk groups. Lung cancer incidence in the high risk group had declined by 68.2% through risk transfer of model-based simulation, which had declined by 56.9% in the medium risk group. Through simulative intervention and risk transition deduced from the DQN model, lung cancer incidences of the high risk, medium risk, and low risk groups were obviously decreased. CONCLUSIONS: A DQN-based deep reinforcement learning model was proposed and validated to develop and simulate a risk transfer pathway of lung cancer among middle-aged and older individuals. Risk stratification supplied an effective foundation for lung cancer risk transition.
References
- 1.Chen, S., & Wu, S. (2026). Research on Risk Transfer Pathways for Lung Cancer Among Middle-Aged and Older Individuals Using Deep Reinforcement Learning: Retrospective Cohort Study. JMIR Medical Informatics. https://doi.org/10.2196/74990
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