AUTHOR=Liu Yufan , Wang Zihang , Cao Xiaowen , Liu Miaoyan , Zhong Lou 

TITLE=Machine learning models for predicting survival in lung cancer patients undergoing microwave ablation

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1561083

DOI=10.3389/fmed.2025.1561083

ISSN=2296-858X

ABSTRACT=ObjectiveTo develop and validate predictive models assessing survival outcomes in patients with non-small cell lung cancer (NSCLC) treated with microwave ablation (MWA), enabling clinical decision support and personalized care.MethodsThis retrospective study analyzed data from 181 NSCLC patients who underwent MWA between May 2013 and May 2023. Prognostic factors were identified through univariate analysis, and predictive models were constructed using machine learning techniques. The model validation was conducted using cross-validation to ensure the model’s robustness and generalizability.ResultsUnivariate analysis revealed several significant prognostic factors, including tumor stage, serum phosphorus levels, patient age, average hemoglobin levels, ground-glass opacities (GGO), and pleural traction. The presence of GGO and pleural traction was associated with worse prognosis, and these factors were incorporated into the model. After training, the best-performing model achieved an area under the curve (AUC) of 0.742, demonstrating a good balance between sensitivity and specificity. Cross-validation and external validation further confirmed the robustness and generalizability of the model, with similar AUC values observed in both validation cohorts. The model effectively predicted the 1-, 3-, and 5-year survival rates for NSCLC patients treated with MWA. These findings suggest that the model can serve as a reliable tool for clinical decision-making and support individualized treatment strategies.ConclusionThe developed predictive model effectively assesses prognosis in NSCLC patients treated with MWA, supporting individualized treatment strategies and improving clinical decision-making.