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Войти в исследования European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology · 2026
Machine learning-based model for predicting post-operative complications and evaluating long-term survival in elderly patients undergoing VATS for early-stage lung adenocarcinoma.
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Xie Qichang, Huang Cheng, Zhan Junpeng, Chen Yiming, Ou Jihang, Huang Qinzhao, Liang Mingqiang, Chen Hao, Scmid Ralph A, Chen Chun, Zheng Bin, Xu Guobing, Zhu Yong, Yang Zhang
Аннотация
BACKGROUND: Postoperative complications remain a major challenge in elderly patients undergoing thoracoscopic resection for early-stage lung adenocarcinoma. Existing risk models insufficiently capture the multidimensional interplay between pulmonary functional reserve and systemic inflammatory response.
METHODS: We conducted a retrospective cohort study including patients aged ≥70 years with stage I-II early-stage lung adenocarcinoma undergoing video-assisted thoracoscopic surgery between 2016 and 2022. Patients with chronic obstructive pulmonary disease (COPD) were excluded, and the study cohort included individuals with both PRISm and normal spirometry. Preoperative spirometry and inflammatory biomarkers obtained within 1 week before surgery were collected as candidate features for the machine learning models. PRISm was defined as preserved FEV1/FVC with reduced FEV1%pred and/or FVC%pred. The primary outcome was post-operative complications (Clavien-Dindo grade ≥2), and secondary outcomes included overall survival and disease-free survival. Seven machine learning models were developed using cross-validation and evaluated for discrimination, calibration, and clinical utility. Model interpretability was assessed using SHAP.
RESULTS: Of 319 patients, 18.5% developed postoperative complications (Clavien-Dindo grade ≥2). PRISm emerged as a significant predictor of both short- and long-term outcomes, being associated with postoperative complications as well as all-cause mortality and disease-free survival. Among the machine-learning algorithms evaluated, XGBoost showed the best predictive performance for postoperative complications (test AUC, 0.786) and provided consistent clinical net benefit. Feature-importance and SHAP analyses further identified PRISm, predicted FEV1, and systemic inflammatory indices as key contributors to model prediction, with nonlinear interactions between impaired pulmonary function and systemic inflammation.
CONCLUSIONS: PRISm was associated with both postoperative complications and long-term survival in elderly patients undergoing VATS for early-stage lung adenocarcinoma. Incorporating pulmonary function and systemic inflammatory indices into machine-learning models may improve perioperative risk stratification, while PRISm may provide additional prognostic information for both short- and long-term outcomes.
METHODS: We conducted a retrospective cohort study including patients aged ≥70 years with stage I-II early-stage lung adenocarcinoma undergoing video-assisted thoracoscopic surgery between 2016 and 2022. Patients with chronic obstructive pulmonary disease (COPD) were excluded, and the study cohort included individuals with both PRISm and normal spirometry. Preoperative spirometry and inflammatory biomarkers obtained within 1 week before surgery were collected as candidate features for the machine learning models. PRISm was defined as preserved FEV1/FVC with reduced FEV1%pred and/or FVC%pred. The primary outcome was post-operative complications (Clavien-Dindo grade ≥2), and secondary outcomes included overall survival and disease-free survival. Seven machine learning models were developed using cross-validation and evaluated for discrimination, calibration, and clinical utility. Model interpretability was assessed using SHAP.
RESULTS: Of 319 patients, 18.5% developed postoperative complications (Clavien-Dindo grade ≥2). PRISm emerged as a significant predictor of both short- and long-term outcomes, being associated with postoperative complications as well as all-cause mortality and disease-free survival. Among the machine-learning algorithms evaluated, XGBoost showed the best predictive performance for postoperative complications (test AUC, 0.786) and provided consistent clinical net benefit. Feature-importance and SHAP analyses further identified PRISm, predicted FEV1, and systemic inflammatory indices as key contributors to model prediction, with nonlinear interactions between impaired pulmonary function and systemic inflammation.
CONCLUSIONS: PRISm was associated with both postoperative complications and long-term survival in elderly patients undergoing VATS for early-stage lung adenocarcinoma. Incorporating pulmonary function and systemic inflammatory indices into machine-learning models may improve perioperative risk stratification, while PRISm may provide additional prognostic information for both short- and long-term outcomes.