Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models
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 can...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Medknow Publications
2025-05-01
|
| Series: | Journal of Medical Signals and Sensors |
| Subjects: | |
| Online Access: | https://journals.lww.com/jmss/fulltext/2025/05010/radiomics_analysis_on_computed_tomography_images.3.aspx |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849705507699294208 |
|---|---|
| author | Farzaneh Ansari Ali Neshasteh-Riz Reza Paydar Fathollah Mohagheghi Sahar Felegari Manijeh Beigi Susan Cheraghi |
| author_facet | Farzaneh Ansari Ali Neshasteh-Riz Reza Paydar Fathollah Mohagheghi Sahar Felegari Manijeh Beigi Susan Cheraghi |
| author_sort | Farzaneh Ansari |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9dc8c28ad0c94eec892a3399fcb0fe7b |
| institution | DOAJ |
| issn | 2228-7477 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Signals and Sensors |
| spelling | doaj-art-9dc8c28ad0c94eec892a3399fcb0fe7b2025-08-20T03:16:28ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772025-05-01155141410.4103/jmss.jmss_51_24Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning ModelsFarzaneh AnsariAli Neshasteh-RizReza PaydarFathollah MohagheghiSahar FelegariManijeh BeigiSusan CheraghiBackground: 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.https://journals.lww.com/jmss/fulltext/2025/05010/radiomics_analysis_on_computed_tomography_images.3.aspxchemotherapy echocardiographyheart failuremachine learningradiomicsradiotherapy |
| spellingShingle | Farzaneh Ansari Ali Neshasteh-Riz Reza Paydar Fathollah Mohagheghi Sahar Felegari Manijeh Beigi Susan Cheraghi Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models Journal of Medical Signals and Sensors chemotherapy echocardiography heart failure machine learning radiomics radiotherapy |
| title | Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models |
| title_full | Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models |
| title_fullStr | Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models |
| title_full_unstemmed | Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models |
| title_short | Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models |
| title_sort | radiomics analysis on computed tomography images for prediction of chemoradiation induced heart failure in breast cancer by machine learning models |
| topic | chemotherapy echocardiography heart failure machine learning radiomics radiotherapy |
| url | https://journals.lww.com/jmss/fulltext/2025/05010/radiomics_analysis_on_computed_tomography_images.3.aspx |
| work_keys_str_mv | AT farzanehansari radiomicsanalysisoncomputedtomographyimagesforpredictionofchemoradiationinducedheartfailureinbreastcancerbymachinelearningmodels AT alineshastehriz radiomicsanalysisoncomputedtomographyimagesforpredictionofchemoradiationinducedheartfailureinbreastcancerbymachinelearningmodels AT rezapaydar radiomicsanalysisoncomputedtomographyimagesforpredictionofchemoradiationinducedheartfailureinbreastcancerbymachinelearningmodels AT fathollahmohagheghi radiomicsanalysisoncomputedtomographyimagesforpredictionofchemoradiationinducedheartfailureinbreastcancerbymachinelearningmodels AT saharfelegari radiomicsanalysisoncomputedtomographyimagesforpredictionofchemoradiationinducedheartfailureinbreastcancerbymachinelearningmodels AT manijehbeigi radiomicsanalysisoncomputedtomographyimagesforpredictionofchemoradiationinducedheartfailureinbreastcancerbymachinelearningmodels AT susancheraghi radiomicsanalysisoncomputedtomographyimagesforpredictionofchemoradiationinducedheartfailureinbreastcancerbymachinelearningmodels |