Abstract
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.
Data availability
Because of ethical and privacy considerations, the raw data are not publicly accessible. However, the datasets generated and analyzed during this study can be obtained from the corresponding author upon reasonable request.
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Acknowledgements
The authors thank Furkan Aydos and Hatice Rüveyda Akça for their support in the preparation of this study.
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This study was conducted without any external financial support.
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Conceptualization: Ugur Ozberk, Selin Akturk Esen; Methodology: Ugur Ozberk, Selin Akturk Esen; Formal analysis and investigation: Ugur Ozberk, Serkan Keskin, Hilal Arslan, Melike Cobankaya; Data curation: Ugur Ozberk, Serkan Keskin, Oznur Bal, Efnan Algın; Writing - original draft preparation: Ugur Ozberk; Writing - review and editing: Selin Akturk Esen, Burak Bilgin, Mehmet Ali Nahit Sendur, Dogan Uncu; Resources: Oznur Bal, Efnan Algın, Burak Bilgin; Supervision: Mehmet Ali Nahit Sendur, Dogan Uncu.
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The study was approved by the Clinical Research Ethics Committee of Ankara City Hospital (Decision No: TABED 1/1772/2025, Date: 22/10/2025) and was conducted in accordance with the principles of the Declaration of Helsinki.
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was waived by the Clinical Research Ethics Committee of Ankara City Hospital due to the retrospective design of the study and the use of anonymized data obtained from medical records.
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Ozberk, U., Esen, S.A., Arslan, H. et al. Machine learning–based prediction of recurrence after curative resection in non–small cell lung cancer. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47862-3
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DOI: https://doi.org/10.1038/s41598-026-47862-3
