ArticleBMC medicine2025
Early detection of non-small cell lung cancer: an electronic health record data-driven approach.
Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction for lung cancer screening: a systematic review and meta-regression.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
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Authors and funding
13 authors.
Funding
Abstract
backgroundSpecific 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 record (EHR) data are crucial for supporting these personalized approaches, improving cancer prevention and early diagnosis. We leverage EHR data and build a prediction model for early detection of non-small cell lung cancer (NSCLC).
methodsWe 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.
resultsWe 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 receiver operating characteristic (ROC) curve (area under the curve, AUC) of 0.801 (positive predictive value (PPV) 0.0173 with specificity 0.02) for predicting 1-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.
conclusionsThis 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.
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