Data Sheet 1_Development and internal validation of a machine-learning model for 3-year mortality after lung cancer resection: an online risk calculator and nomogram.zip

This proportion describes the event composition of the selected analytic cohort and should not be interpreted as a population-level postoperative mortality estimate. Six predictors were retained by LASSO: T stage, airway dissemination, pleural invasion, Ki-67 index, BMI, and direct bilirubin. Random Forest showed perfect apparent discrimination in the training set (AUC = 1.00) but decreased to 0.73 in validation.

Logistic regression achieved the highest validation AUC (0.79; 95% CI, 0.70–0.87), with an accuracy of 0.75, an F1 score of 0.73, and a Brier score of 0.187, and was therefore selected as the final model.</p>Conclusion<p>The final logistic regression model provided moderate internal discrimination and an interpretable framework for exploratory 3-year mortality risk estimation after lung cancer resection. The nomogram and web-based calculator facilitated model visualization, but the outcome-ascertained analytic cohort, potential event enrichment, and absence of external validation limited the interpretation of absolute risk and direct clinical application.

Therefore, an independent multicenter and prospective validation with appropriate recalibration is required.</p>