Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis
Background: Radiation-induced esophagitis remains a significant challenge in thoracic and neck cancer treatment, impacting patient quality of life and potentially limiting therapeutic efficacy. This study aimed to develop and validate a decision tree-based model for predicting acute esophagitis grad...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Biochemistry and Biophysics Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405580825000780 |
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| author | Mostafa Alizade-Harakiyan Amin Khodaei Ali Yousefi Hamed Zamani Asghar Mesbahi |
| author_facet | Mostafa Alizade-Harakiyan Amin Khodaei Ali Yousefi Hamed Zamani Asghar Mesbahi |
| author_sort | Mostafa Alizade-Harakiyan |
| collection | DOAJ |
| description | Background: Radiation-induced esophagitis remains a significant challenge in thoracic and neck cancer treatment, impacting patient quality of life and potentially limiting therapeutic efficacy. This study aimed to develop and validate a decision tree-based model for predicting acute esophagitis grades in patients undergoing chemoradiotherapy. Methods: Data from 100 patients receiving thoracic and neck radiotherapy were analyzed. The dataset comprised 33 features, including demographic, clinical, and dosimetric parameters. A decision tree classifier was implemented for both binary (Grade ≥2 vs. <2) and multi-class (Grades 1, 2, and 3) classification. Model performance was evaluated using standard metrics including accuracy, precision, recall, and F1-score. Results: The binary classification model achieved 97 % accuracy in distinguishing acute esophagitis. The multi-class model demonstrated 98 % accuracy in predicting specific grades. Key predictive features included V40 (volume receiving 40 Gy), V60, and average esophageal dose. The model generated interpretable decision rules, with V60 ≥ 2.3 strongly indicating Grade 3 esophagitis. Conclusions: The decision tree model demonstrates high accuracy in predicting radiation-induced esophagitis grades while maintaining clinical interpretability. This approach offers potential for treatment optimization and personalized risk assessment in radiotherapy planning. The model's transparency and reliability make it a promising tool for clinical decision support in radiation oncology. |
| format | Article |
| id | doaj-art-8b7b4782508b46e5adb7386f852ed708 |
| institution | OA Journals |
| issn | 2405-5808 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biochemistry and Biophysics Reports |
| spelling | doaj-art-8b7b4782508b46e5adb7386f852ed7082025-08-20T02:37:38ZengElsevierBiochemistry and Biophysics Reports2405-58082025-06-014210199110.1016/j.bbrep.2025.101991Decision tree-based machine learning algorithm for prediction of acute radiation esophagitisMostafa Alizade-Harakiyan0Amin Khodaei1Ali Yousefi2Hamed Zamani3Asghar Mesbahi4Department of Radiation Oncology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranDepartment of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, IranMedical Physics Department, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, IranMedical Physics Department, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran; Molecular Medicine Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Corresponding author. Medical Physics Department, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.Background: Radiation-induced esophagitis remains a significant challenge in thoracic and neck cancer treatment, impacting patient quality of life and potentially limiting therapeutic efficacy. This study aimed to develop and validate a decision tree-based model for predicting acute esophagitis grades in patients undergoing chemoradiotherapy. Methods: Data from 100 patients receiving thoracic and neck radiotherapy were analyzed. The dataset comprised 33 features, including demographic, clinical, and dosimetric parameters. A decision tree classifier was implemented for both binary (Grade ≥2 vs. <2) and multi-class (Grades 1, 2, and 3) classification. Model performance was evaluated using standard metrics including accuracy, precision, recall, and F1-score. Results: The binary classification model achieved 97 % accuracy in distinguishing acute esophagitis. The multi-class model demonstrated 98 % accuracy in predicting specific grades. Key predictive features included V40 (volume receiving 40 Gy), V60, and average esophageal dose. The model generated interpretable decision rules, with V60 ≥ 2.3 strongly indicating Grade 3 esophagitis. Conclusions: The decision tree model demonstrates high accuracy in predicting radiation-induced esophagitis grades while maintaining clinical interpretability. This approach offers potential for treatment optimization and personalized risk assessment in radiotherapy planning. The model's transparency and reliability make it a promising tool for clinical decision support in radiation oncology.http://www.sciencedirect.com/science/article/pii/S2405580825000780Radiation-induced esophagitisDecision tree classifierPredictive modelingMachine learningRadiotherapyTreatment planning |
| spellingShingle | Mostafa Alizade-Harakiyan Amin Khodaei Ali Yousefi Hamed Zamani Asghar Mesbahi Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis Biochemistry and Biophysics Reports Radiation-induced esophagitis Decision tree classifier Predictive modeling Machine learning Radiotherapy Treatment planning |
| title | Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis |
| title_full | Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis |
| title_fullStr | Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis |
| title_full_unstemmed | Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis |
| title_short | Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis |
| title_sort | decision tree based machine learning algorithm for prediction of acute radiation esophagitis |
| topic | Radiation-induced esophagitis Decision tree classifier Predictive modeling Machine learning Radiotherapy Treatment planning |
| url | http://www.sciencedirect.com/science/article/pii/S2405580825000780 |
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