Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models - Farzaneh Ansari; Ali Neshasteh-Riz; Reza Paydar; Fathollah Mohagheghi; Sahar Felegari; Manijeh Beigi; Susan Cheraghi (Journal of Medical Signals and Sensors)
Background: This study aimed to evaluate the effectiveness of clinical, dosimetric, and radiomic features from computed tomography (CT) scans in predicting the probability of heart failure in breast cancer patients undergoing chemoradiation treatment. Materials and Methods: We selected 54 breast cancer patients who received left-sided chemoradiation therapy and had a low risk of natural heart failure according to the Framingham score. We compared echocardiographic patterns and ejection fraction (EF) measurements before and 3 years after radiotherapy for each patient. Based on these comparisons, we evaluated the incidence of heart failure 3 years postchemoradiation therapy. For machine learning (ML) modeling, we first segmented the heart as the region of interest in CT images using a deep learning technique. We then extracted radiomic features from this region. We employed three widely used classifiers – decision tree, K-nearest neighbor, and random forest (RF) – using a combination of radiomic, dosimetric, and clinical features to predict chemoradiation-induced heart failure. The evaluation criteria included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (area under the curve [AUC]). Results: In this study, 46% of the patients experienced heart failure, as indicated by EF. A total of 873 radiomic features were extracted from the segmented area. Out of 890 combined radiomic, dosimetric, and clinical features, 15 were selected. The RF model demonstrated the best performance, with an accuracy of 0.85 and an AUC of 0.98. Patient age and V5 irradiated heart volume were identified as key predictors of chemoradiation-induced heart failure. Conclusion: Our quantitative findings indicate that employing ML methods and combining radiomic, dosimetric, and clinical features to identify breast cancer patients at risk of cardiotoxicity is feasible.