Rationale: Specific patient characteristics increase the risk of cancer, necessitating personalized healthcare approaches. For high-risk individuals, tailored clinical management ensures proactive monitoring and timely interventions. Electronic Health Records (EHR) data are crucial for supporting these personalized approaches, improving cancer prevention and early diagnosis. Objectives: We leverage EHR data and build a prediction model for early detection of non-small cell lung cancer (NSCLC). Methods: We utilize data from Mass General Brigham's EHR and implement a three-stage ensemble learning approach. Initially, we generate risk scores using multivariate logistic regression in a self-control and case-control design to distinguish between cases and controls. Subsequently, these risk scores are integrated and calibrated using a prospective Cox model to develop the risk prediction model. Results: We identified 127 EHR-derived features predictive for early detection of NSCLC. The highly predictive features include smoking, relevant lab test results, and chronic lung diseases. The predictive model reached area under the ROC curve (AUC) of 0.801 (positive predictive value (PPV) 0.0173 with specificity 0.02) for predicting one-year NSCLC risk in a population aged 18 and above, and AUC of 0.757 (PPV 0.0196 with specificity 0.02) in a population aged 40 and above. Conclusions: This study identified EHR derived features which are predictive of early NSCLC diagnosis. The developed risk prediction model exhibits superior performance for early detection of NSCLC compared to a baseline model that only relies on demographic and smoking information, demonstrating the potential of incorporating EHR derived features for personalized cancer screening recommendations and early detection.

The authors have declared no competing interest.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study utilizes de-identified data from the Mass General Brigham (MGB) Health System and is overseen by the MGB Institutional Review Board (IRB).

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Patient-level data are protected due to privacy concerns, and all study results including model parameters in the present study are available upon reasonable request to the authors.