Predicting overall survival of NSCLC patients with clinical, radiomics and deep learning features

Accurate estimation of Overall Survival (OS) in Non-Small Cell Lung Cancer (NSCLC) patients provides critical insights for treatment planning. While previous studies have shown that radiomics or Deep Learning (DL) features improved prediction accuracy, this study aimed to evaluate whether a model that integrates clinical, radiomics, DL, and dosimetric features outperforms other models developed with only a subset of these features. We collected pre-treatment lung CT scans and clinical data for 219 NSCLC patients from the Maastro Clinic: 183 for training and 36 for testing. Radiomics features were extracted using the Python radiomics feature extractor, and DL and dose features were obtained using a 3D ResNet model. An ensemble model comprising XGB and NN classifiers was developed using: (1) clinical features only; (2) clinical and radiomics features; (3) clinical and DL features; (4) clinical and dose features, and (5) clinical, radiomics, dose and DL features. The performance metrics were evaluated for the test and K-fold cross-validation data sets. The prediction model utilizing only clinical variables provided an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.71 and a test accuracy of 72.73%. The best performance came from combining clinical, radiomics, dose and DL features (AUC: 0.84, accuracy: 88.64%). World Health Organisation Performance Status emerged as the factor with the highest importance for the combined model. Integrating radiomics, dose and DL features with clinical characteristics improved the prediction of OS after radiotherapy for NSCLC patients. The increased accuracy of our integrated model could enable personalized, risk-based treatment planning, guiding clinicians toward more effective interventions, improved patient outcomes and enhanced quality of life.