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<p>Recurrence after curative resection remains a major clinical challenge in non–small cell lung cancer (NSCLC), and improved postoperative risk stratification is needed. Machine learning (ML) approaches may enhance recurrence prediction using routinely available clinicopathologic data. We analyzed 265 patients who underwent curative lung cancer surgery. Recurrence was the primary endpoint. Seventeen clinical, pathological, and treatment-related variables were evaluated. Multiple supervised ML classifiers were trained using the full dataset and reduced feature sets generated by ANOVA, chi-square, and Kruskal–Wallis methods. Model performance was assessed using accuracy, area under the curve (AUC), and F1 score. Prognostic factors were examined with Cox regression, and model interpretability was explored through feature importance and SHAP analysis. Recurrence occurred in 82 patients (30.9%). AdaBoost achieved the highest accuracy (0.79) and F1 score (0.87), whereas SVC-RBF showed the highest AUC (0.81). Performance remained stable across feature-selection strategies. Histologic subtype, tumor size, tumor grade, and ECOG performance status were consistently influential variables, with ECOG status and tumor size dominating SHAP-based predictions. These findings indicate that ML models using routine clinicopathologic variables can reliably predict recurrence after NSCLC surgery and support individualized postoperative risk assessment.</p>

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Machine learning–based prediction of recurrence after curative resection in non–small cell lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月9日

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  • Ugur Ozberk,
  • Selin Akturk Esen,
  • Hilal Arslan,
  • Oznur Bal,
  • Efnan Algın,
  • Serkan Keskin,
  • Burak Bilgin,
  • Melike Cobankaya,
  • Mehmet Ali Nahit Sendur,
  • Dogan Uncu

摘要

Recurrence after curative resection remains a major clinical challenge in non–small cell lung cancer (NSCLC), and improved postoperative risk stratification is needed. Machine learning (ML) approaches may enhance recurrence prediction using routinely available clinicopathologic data. We analyzed 265 patients who underwent curative lung cancer surgery. Recurrence was the primary endpoint. Seventeen clinical, pathological, and treatment-related variables were evaluated. Multiple supervised ML classifiers were trained using the full dataset and reduced feature sets generated by ANOVA, chi-square, and Kruskal–Wallis methods. Model performance was assessed using accuracy, area under the curve (AUC), and F1 score. Prognostic factors were examined with Cox regression, and model interpretability was explored through feature importance and SHAP analysis. Recurrence occurred in 82 patients (30.9%). AdaBoost achieved the highest accuracy (0.79) and F1 score (0.87), whereas SVC-RBF showed the highest AUC (0.81). Performance remained stable across feature-selection strategies. Histologic subtype, tumor size, tumor grade, and ECOG performance status were consistently influential variables, with ECOG status and tumor size dominating SHAP-based predictions. These findings indicate that ML models using routine clinicopathologic variables can reliably predict recurrence after NSCLC surgery and support individualized postoperative risk assessment.