Objective: Clinical stage IA non-small cell lung cancer (NSCLC) showing a pure-solid appearance on computed tomography is associated with a worse prognosis. This study aimed to develop and validate machine-learning models using preoperative clinical and radiomic features to predict overall survival (OS) in clinical stage IA pure-solid NSCLC.
Methods: Patients who underwent lung resection for NSCLC between January 2012 and December 2020 were reviewed. The radiomic features were extracted from the intratumoral and peritumoral regions on computed tomography. The machine-learning models were developed using random survival forest and eXtreme Gradient Boosting (XGBoost) algorithms, whereas the Cox regression model was set as a benchmark. Model performance was assessed using the integrated time-dependent area under the curve (iAUC) and validated by 5-fold cross-validation.
Results: In total, 642 patients with clinical stage IA pure-solid NSCLC were included. Among 3748 radiomic and 34 preoperative clinical features, 42 features were selected. Both machine-learning models outperformed the Cox regression model (iAUC, 0.753; 95% confidence interval [CI], 0.629-0.829). The XGBoost model showed a better performance (iAUC, 0.832; 95% CI, 0.779-0.880) than the random survival forest model (iAUC, 0.795; 95% CI, 0.734-0.856). The XGBoost model showed an excellent survival stratification performance with a significant OS difference among the low-risk (5-year OS, 100.0%), moderate low-risk (5-year OS, 88.5%), moderate high-risk (5-year OS, 75.6%), and high-risk (5-year OS, 41.7%) groups (P < .0001).
Conclusions: A radiomics-based machine-learning model can preoperatively and accurately predict OS and improve survival stratification in clinical stage IA pure-solid NSCLC.
Keywords: clinical stage IA; machine learning; non–small cell lung cancer; pure-solid; radiomics; survival.
Copyright © 2024 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.