European journal of radiology · 2025
Radiomics enhance the prediction of endovascular treatment success for femoropopliteal chronic total occlusions: a proof-of-concept study.
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Vrettos Konstantinos, Kasioumi Konstantina, Galanakis Nikolaos, Kehagias Elias, Kontopodis Nikolaos, Matthaiou Nikolas, Klontzas Michail E
Аннотация
OBJECTIVE: This study aims to investigate if integrating radiomics features with clinical data can enhance the performance of an AI model that predicts surgical outcomes among patients undergoing interventional radiology procedures, such as endovascular crossing for chronic total occlusions (CTOs) in peripheral arteries. The proposed model and all code are open-access.
METHODS: The dataset included 76 retrospectively collected patients with symptomatic peripheral arterial disease, specifically those with CTOs in the superficial femoral artery (SFA) and/or popliteal artery (POPA). Four machine learning architectures: CatBoost, Random Forest, XGBoost, and TabPFN were evalauted on three datasets: clinical data (A), radiomics features (B) and combined (C). Model performance was evaluated using Matthews Correlation Coefficient (MCC) due to class imbalance, with additional metrics like AUC and F1-score reported for comparison. SHAP values were used to interpret the best-performing model's decisions.
RESULTS: The best performing model was the XGBoost trained on the combined dataset, achieving 0.71 MCC, 0.9 specificity and 0.84 (95% CI 0.7-0.9) AUC. The SHAP analysis revealed that the model's output is strongly influenced by both radiomics and clinical features.
CONCLUSION: 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.
METHODS: The dataset included 76 retrospectively collected patients with symptomatic peripheral arterial disease, specifically those with CTOs in the superficial femoral artery (SFA) and/or popliteal artery (POPA). Four machine learning architectures: CatBoost, Random Forest, XGBoost, and TabPFN were evalauted on three datasets: clinical data (A), radiomics features (B) and combined (C). Model performance was evaluated using Matthews Correlation Coefficient (MCC) due to class imbalance, with additional metrics like AUC and F1-score reported for comparison. SHAP values were used to interpret the best-performing model's decisions.
RESULTS: The best performing model was the XGBoost trained on the combined dataset, achieving 0.71 MCC, 0.9 specificity and 0.84 (95% CI 0.7-0.9) AUC. The SHAP analysis revealed that the model's output is strongly influenced by both radiomics and clinical features.
CONCLUSION: 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.