Radiomics enhance the prediction of endovascular treatment success for femoropopliteal chronic total occlusions: a proof-of-concept study

The integration of radiomics and clinical data has shown significant potential in improving the performance of AI models that predict outcomes of interventional radiology procedures. Among the tested architectures, XGBoost demonstrated superior performance when trained on a combined dataset featuring both radiomics and clinical data. Importantly, the AI model also managed to correctly identify the most clinically important parameters from the data. Moving forward, expanding the sample size and addressing data imbalance could further validate these findings in diverse patient populations.