An optimized XGBoost model integrating clinical, environmental, and genomic data achieved an AUROC of 0.879 for predicting incident lung cancer, outperforming linear and deep-learning baselines.
Cohort (n=7,151)
No
Can an integrated model leveraging EHR, environmental, and genomic data accurately predict incident lung cancer diagnosis in adults?
An integrated gradient-boosting model combining clinical, environmental, and genomic data can accurately stratify individual lung cancer risk.
Abstract Background: Lung cancer risk reflects intersecting clinical, environmental, and genomic factors, yet these data types are rarely integrated at the individual level. We assembled an EHR-based cohort in Southern California to build a predictive model of incident lung cancer diagnosis and to characterize the clinical, genomic, and neighborhood features that drive risk. Methods: We constructed a retrospective cohort of 7,151 adults from UC San Diego Health electronic health records (50.4% female, 65% ≥ 65 years) and linked approximately 40 clinical features to census-tract-level environmental and socioeconomic indicators from CalEnviroScreen, as well as genomic mutation status for ALK and EGFR. We compared 14 classifiers (logistic regression, random forest, XGBoost, and 11 PyTorch neural networks) using stratified five-fold cross-validation to predict lung cancer diagnosis. Hyperparameters for top performing models were optimized using Bayesian search in Optuna. Model performance was summarized using AUROC, accuracy, precision, recall, and F1, and feature importance was assessed using Shapley (SHAP) values. Results: Optimized XGBoost achieved the best cross-validated discrimination (AUROC 0.879), with accuracy 0.802, precision 0.744, and F1 0.694, outperforming linear and deep-learning baselines. Top-ranked features by SHAP included smoking intensity, cardiometabolic comorbidity, and age, with neighborhood unemployment, pesticide burden, and ozone levels contributing additional, though smaller, predictive signal, reinforcing the contribution of neighborhood disadvantage and pollution to lung cancer vulnerability in this regional cohort. Among genomically profiled patients, ALK-positive cases were diagnosed at a significantly younger age than ALK-wildtype cases (mean 56 vs 71 years, p=0.016), underscoring biologically distinct disease courses. Conclusion: An integrated gradient-boosting model leveraging EHR, environmental, and genomic data can meaningfully stratify individual lung cancer risk in a diverse regional cohort and elevate both clinical and neighborhood drivers of vulnerability. These findings support the use of routinely collected health and environmental data to guide targeted lung cancer screening and prevention efforts and motivate future work on external validation, time-varying exposures, and explicit fairness constraints across racial and socioeconomic groups. Citation Format: Gianni Pucillo, Sanye Naqvi, Allison Jue, Chandler Law, Sandip Patel, Uduak Z. George. Combining electronic health records, environmental, and genomics data for lung cancer risk prediction in Southern California abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7594.
Pucillo et al. (Fri,) conducted a cohort in Lung cancer (n=7,151). XGBoost predictive model combining EHR, environmental, and genomics data vs. Linear and deep-learning baselines was evaluated on Incident lung cancer diagnosis prediction (AUROC). An optimized XGBoost model integrating clinical, environmental, and genomic data achieved an AUROC of 0.879 for predicting incident lung cancer, outperforming linear and deep-learning baselines.