{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T13:19:06Z","timestamp":1787663946232,"version":"build-2736575974"},"reference-count":36,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Surgical resection is the primary curative option for elderly patients with non\u2010small cell lung cancer (NSCLC), but its survival benefit remains debated due to comorbidities and competing risks. This study evaluated the dynamic impact of surgery on long\u2010term survival and developed machine learning models for prognostic prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 259,701 elderly NSCLC patients (\u2265\u200960\u2009years) from the SEER database (2000\u20132021). Patients with missing disease stage or tumor size were strictly excluded. Propensity score matching (1:1, caliper\u2009=\u20090.05), comprehensively adjusting for age, sex, stage, tumor size, chemotherapy, and radiotherapy to produce a highly balanced cohort of 15,946 patients (7973 per group). Multivariable Cox regression, piecewise time\u2010dependent Cox models, and stage\u2010stratified Kaplan\u2013Meier curves were used to assess surgical effects. Five machine learning algorithms were trained to predict 5\u2010year overall survival.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Surgery was independently associated with a significant overall survival benefit (adjusted HR\u2009=\u20090.45,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Time\u2010dependent piecewise Cox analysis revealed this protective effect was most profound within the first 2\u2009years post\u2010diagnosis (HR\u2009=\u20090.408,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and progressively attenuated over long\u2010term follow\u2010up due to increasing competing risks. While a statistical survival advantage was observed across all stages, advanced\u2010stage results are likely confounded by selection bias. Among machine learning models predicting 5\u2010year survival, Logistic Regression achieved the highest discriminative performance (AUC\u2009=\u20090.724), closely followed by Random Forest (AUC\u2009=\u20090.719) and XGBoost (AUC\u2009=\u20090.700). SHAP analysis confirmed surgical resection, baseline stage, and tumor size as the most critical prognostic determinants.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Surgical resection was significantly associated with a profound but time\u2010attenuating survival benefit in elderly NSCLC patients. However, the observed statistical benefit in advanced stages must be interpreted with extreme caution due to unmeasured selection bias and should not replace individualized multidisciplinary evaluations. Logistic Regression and tree\u2010based models provide robust 5\u2010year survival predictions, highlighting the value of machine learning for individualized risk stratification in geriatric oncology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.72186","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:07:27Z","timestamp":1787029647000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Impact of Surgical Resection on 5\u2010Year Survival in Elderly\n                    <scp>NSCLC<\/scp>\n                    Patients: A\n                    <scp>SEER<\/scp>\n                    Database Analysis With Machine Learning\u2010Based Predictive Modeling in Patients Aged \u2265\u200960 (2000\u20132021)"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8888-2724","authenticated-orcid":false,"given":"Bo","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Thoracic Surgery Sichuan Provincial People's Hospital East Sichuan Hospital &amp;Dazhou First People's Hospital  Chengdu Sichuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1513-9811","authenticated-orcid":false,"given":"Xiangchun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Clinical Medicine, Li Ka Shing Faculty of Medicine The University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baowei","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery Sichuan Provincial People's Hospital East Sichuan Hospital &amp;Dazhou First People's Hospital  Chengdu Sichuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huachuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery Sichuan Provincial People's Hospital East Sichuan Hospital &amp;Dazhou First People's Hospital  Chengdu Sichuan 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