LANTERN-01: AI model for Postoperative Complications Prediction after NSCLC Lung Resection: Prospective Multicentric Study and Extern Validation

Abstract Introduction : Artificial intelligence(AI) techniques may combine various omics datasets to create more accurate predictive models for lung cancer patients management. The aim of this study from the LANTERN project, is to develop an AI-based predictive model of post-operative complications after lung resection for NSCLC. Methods In the framework of LANTERN Consortium we prospectively collected data from 3/2023 to 12/2024 of patients who underwent curative surgery for Stage I-III NSCLC and were herein analyzed considering 80 preoperative clinical features and 43 spirometric variables to predict the occurrence of post-operative complications. Prediction models were developed on the basis of different feature selection algorithms and machine-learning models. An external dataset composed by a surgical series of 232 patients was used for validation. Results The final analysis was conducted on 231 surgical patients. Post-operative complications were observed in 37 patients (16%). AI-based models showed that pathologic score (AUC = 0.72, 95% CI [0.62–0.81]) and pre-opFEV1_TEOR (AUC = 0.77, CI [0.67–0.89]) was the most relevant variables. Testing the models on the external dataset, while the predictive value of Pathologic score alone was reduced, pre-opFEV1_TEOR and the combination of Pathologic score and pre-opFEV1_TEOR were confirmed to predict post-op outcome. Postoperative risk rises by about 6% per pathologic score level and 11–12% for each 10% decrease in FEV1_TEOR. Conclusions Combining the FEV1_TEOR and the pathologic score permits to predict the complications risk in significant way. This model could be tested in a further prospective cohort of patients to verify its effectiveness in order to improve the perioperative management of lung resection candidates.