A pilot study was conducted to evaluate the feasibility and performance of a predictive model for treatment responses in mesothelioma patients, leveraging radiomics and machine learning. Radiomics and delta-radiomics (∆radiomics) features from CT scans were analyzed for reproducibility and informativeness, identifying key features for training a random forest classifier. The model achieved an accuracy of 0.75–0.9 in predicting pleural tumor responses, supporting the design of future studies involving 250–400 tumors. This study demonstrated the reproducibility and effectiveness of radiomics/∆radiomics in relation to tumor localization, emphasizing the need for multiple tumor models to create an integrated patient model.

Published in Cancers 2025, 17, 463.