Key result
A Random Forest machine learning model combining radiomic features from CT scans and patient age predicted chemoradiation-induced heart failure with an accuracy of 0.85 and an AUC of 0.98.
Why the study?
The study aimed to evaluate the effectiveness of clinical, dosimetric, and CT radiomic features in predicting heart failure probability in breast cancer patients undergoing chemoradiation.
Does a machine learning model using radiomic, dosimetric, and clinical features from CT scans predict chemoradiation-induced heart failure in breast cancer patients?
Cohort (n=54)
Yes
Does a machine learning model using radiomic, dosimetric, and clinical features from CT scans predict chemoradiation-induced heart failure in breast cancer patients?
Machine learning models combining radiomic, dosimetric, and clinical features from CT scans can accurately predict chemoradiation-induced heart failure in breast cancer patients.
May aid risk stratification in breast cancer; hypothesis-generating and requires prospective validation before clinical use.
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.
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Ansari et al. (2025) conducted a cohort in Breast cancer (n=54). Random Forest machine learning model using radiomic, dosimetric, and clinical features vs. Decision Tree and K-Nearest Neighbor models was evaluated on Prediction of chemoradiation-induced heart failure (AUC 0.98). A Random Forest machine learning model combining radiomic features from CT scans and patient age predicted chemoradiation-induced heart failure with an accuracy of 0.85 and an AUC of 0.98.
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