{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T06:00:13Z","timestamp":1790316013594,"version":"4.1.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Lung cancer remains a leading cause of cancer-related mortality, and postoperative risk stratification may support individualized surveillance after pulmonary resection. This study aimed to develop and internally validate an exploratory prediction model for 3-year postoperative mortality using routinely available clinical, pathological, and laboratory variables, while explicitly addressing outcome ascertainment, model calibration, and the limitations of a single-center retrospective cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>LASSO regression was used for feature selection, followed by a comparison of seven algorithms: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Network, XGBoost, and LightGBM. Model discrimination, calibration, and decision-curve performance were evaluated in a 7:3 internal hold-out split. The final model was selected primarily according to an internal-validation AUC, with calibration and classification metrics considered jointly. A web-based calculator and a nomogram were developed from the final model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the final outcome-ascertained analytic sample, 166 of 350 patients (47.43%) were classified as having died within 36 months and 184 (52.57%) were alive beyond 36 months. 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\u2009=\u20091.00) but decreased to 0.73 in validation. Logistic regression achieved the highest validation AUC (0.79; 95% CI, 0.70\u20130.87), 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1815915","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:33:53Z","timestamp":1790314433000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and internal validation of a machine-learning model for 3-year mortality after lung cancer resection: an online risk calculator and nomogram"],"prefix":"10.3389","volume":"13","author":[{"given":"Chen","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Pathology, Tongling People\u2019s Hospital","place":["Tongling, Anhui, China"]},{"name":"Tongling Academy of Medical Sciences","place":["Tongling, Anhui, 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