ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2023
Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care.
Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines 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
- Prediction models for early detection and diagnosis of lung cancer in people who have never smoked: a systematic review and critical appraisal.Cancer causes & control : CCC · 2026Pooled it
- A systematic review and meta-analysis of lung cancer risk prediction models.Acta oncologica (Stockholm, Sweden) · 2025Pooled it
- Validating predictive models on EHRs data : lessons learnt from the NCDR-IMPACT-score applied on a French cardiac catheterization cohort.BMC cardiovascular disorders · 2026Article
- Hybrid Modelling of Pulmonary Cancer Risk Prediction Using Classical Algorithms to Modern Machine Learning Techniques.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Longitudinal multisource clinical model for early lung cancer risk stratification and screening.BMJ health & care informatics · 2026Article
- RELEAP: reinforcement-enhanced label-efficient active phenotyping for electronic health records.JAMIA open · 2026Article
- Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions.Journal of general internal medicine · 2026Article
- Secure and interpretable lung cancer prediction model using mapreduce private blockchain federated learning and XAI.Scientific reports · 2025Article
- Early detection of non-small cell lung cancer: an electronic health record data-driven approach.BMC medicine · 2025Article
- The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative reviewDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Diagnostic tools in respiratory medicine (Review).Biomedical reports · 2025Review
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study.BMC cancer · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Performance of machine learning algorithms for lung cancer prediction: a comparative approach.Scientific reports · 2024Article
- Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed.Diagnostics (Basel, Switzerland) · 2023Review
- Deep Learning in Cytopathology: The Potential for Multimodal Synergy of Clinical and Cytological Input Data.Journal of cytologyReview
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study used machine learning to develop a 3-year lung cancer risk prediction model with large real-world data in a mostly younger population.
methodsOver 4.7 million individuals, aged 45 to 65 years with no history of any cancer or lung cancer screening, diagnostic, or treatment procedures, with an outpatient visit in 2013 were identified in Optum's de-identified Electronic Health Record (EHR) dataset. A least absolute shrinkage and selection operator model was fit using all available data in the 365 days prior. Temporal validation was assessed with recent data. External validation was assessed with data from Mercy Health Systems EHR and Optum's de-identified Clinformatics Data Mart Database. Racial inequities in model discrimination were assessed with xAUCs.
resultsThe model AUC was 0.76. Top predictors included age, smoking, race, ethnicity, and diagnosis of chronic obstructive pulmonary disease. The model identified a high-risk group with lung cancer incidence 9 times the average cohort incidence, representing 10% of patients with lung cancer. Model performed well temporally and externally, while performance was reduced for Asians and Hispanics.
conclusionsA high-dimensional model trained using big data identified a subset of patients with high lung cancer risk. The model demonstrated transportability to EHR and claims data, while underscoring the need to assess racial disparities when using machine learning methods. IMPACT: This internally and externally validated real-world data-based lung cancer prediction model is available on an open-source platform for broad sharing and application. Model integration into an EHR system could minimize physician burden by automating identification of high-risk patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.