Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI
Radiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. SS.BN3 consomic rats, which have a more subtle RIHD ph...
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MDPI AG
2024-12-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/12/308 |
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| author | Dayeong An El-Sayed Ibrahim |
| author_facet | Dayeong An El-Sayed Ibrahim |
| author_sort | Dayeong An |
| collection | DOAJ |
| description | Radiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. SS.BN3 consomic rats, which have a more subtle RIHD phenotype compared to Dahl salt-sensitive (SS) rats, were treated with localized cardiac RT or sham at 10 weeks of age. Cardiac MRI was performed 8 and 10 weeks post-treatment to assess global and regional cardiac function. ML algorithms were applied to differentiate sham-treated and irradiated rats based on early changes in myocardial function. Despite normal global left ventricular ejection fraction in both groups, strain analysis showed significant reductions in the anteroseptal and anterolateral segments of irradiated rats. Gradient boosting achieved an F1 score of 0.94 and an ROC value of 0.95, while random forest showed an accuracy of 88%. These findings suggest that ML, combined with cardiac MRI, can effectively detect early preclinical changes in RIHD, particularly alterations in regional myocardial contractility, highlighting the potential of these techniques for early detection and monitoring of radiation-induced cardiac dysfunction. |
| format | Article |
| id | doaj-art-d5516e70cfab42a39f2e85fda5b8575f |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-d5516e70cfab42a39f2e85fda5b8575f2024-12-27T14:32:32ZengMDPI AGJournal of Imaging2313-433X2024-12-01101230810.3390/jimaging10120308Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRIDayeong An0El-Sayed Ibrahim1Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USADepartment of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USARadiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. SS.BN3 consomic rats, which have a more subtle RIHD phenotype compared to Dahl salt-sensitive (SS) rats, were treated with localized cardiac RT or sham at 10 weeks of age. Cardiac MRI was performed 8 and 10 weeks post-treatment to assess global and regional cardiac function. ML algorithms were applied to differentiate sham-treated and irradiated rats based on early changes in myocardial function. Despite normal global left ventricular ejection fraction in both groups, strain analysis showed significant reductions in the anteroseptal and anterolateral segments of irradiated rats. Gradient boosting achieved an F1 score of 0.94 and an ROC value of 0.95, while random forest showed an accuracy of 88%. These findings suggest that ML, combined with cardiac MRI, can effectively detect early preclinical changes in RIHD, particularly alterations in regional myocardial contractility, highlighting the potential of these techniques for early detection and monitoring of radiation-induced cardiac dysfunction.https://www.mdpi.com/2313-433X/10/12/308radiation-induced heart dysfunctionmachine learning in medical imagingcardiac magnetic resonance imagingmyocardial strain analysiscardiotoxicity |
| spellingShingle | Dayeong An El-Sayed Ibrahim Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI Journal of Imaging radiation-induced heart dysfunction machine learning in medical imaging cardiac magnetic resonance imaging myocardial strain analysis cardiotoxicity |
| title | Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI |
| title_full | Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI |
| title_fullStr | Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI |
| title_full_unstemmed | Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI |
| title_short | Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI |
| title_sort | elucidating early radiation induced cardiotoxicity markers in preclinical genetic models through advanced machine learning and cardiac mri |
| topic | radiation-induced heart dysfunction machine learning in medical imaging cardiac magnetic resonance imaging myocardial strain analysis cardiotoxicity |
| url | https://www.mdpi.com/2313-433X/10/12/308 |
| work_keys_str_mv | AT dayeongan elucidatingearlyradiationinducedcardiotoxicitymarkersinpreclinicalgeneticmodelsthroughadvancedmachinelearningandcardiacmri AT elsayedibrahim elucidatingearlyradiationinducedcardiotoxicitymarkersinpreclinicalgeneticmodelsthroughadvancedmachinelearningandcardiacmri |