Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy
<b>Background/Objectives:</b> Deep inspiration breath-hold (DIBH) is a commonly used technique to reduce the mean heart dose (MHD), which is critical for minimizing late cardiac side effects in breast cancer patients undergoing radiation therapy (RT). Although previous studies have explo...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2075-4418/15/6/668 |
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| author | Wlla E. Al-Hammad Masahiro Kuroda Ghaida Al Jamal Mamiko Fujikura Ryo Kamizaki Kazuhiro Kuroda Suzuka Yoshida Yoshihide Nakamura Masataka Oita Yoshinori Tanabe Kohei Sugimoto Irfan Sugianto Majd Barham Nouha Tekiki Miki Hisatomi Junichi Asaumi |
| author_facet | Wlla E. Al-Hammad Masahiro Kuroda Ghaida Al Jamal Mamiko Fujikura Ryo Kamizaki Kazuhiro Kuroda Suzuka Yoshida Yoshihide Nakamura Masataka Oita Yoshinori Tanabe Kohei Sugimoto Irfan Sugianto Majd Barham Nouha Tekiki Miki Hisatomi Junichi Asaumi |
| author_sort | Wlla E. Al-Hammad |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> Deep inspiration breath-hold (DIBH) is a commonly used technique to reduce the mean heart dose (MHD), which is critical for minimizing late cardiac side effects in breast cancer patients undergoing radiation therapy (RT). Although previous studies have explored the potential of machine learning (ML) to predict which patients might benefit from DIBH, none have rigorously assessed ML model performance across various MHD thresholds and parameter settings. This study aims to evaluate the robustness of ML models in predicting the need for DIBH across different clinical scenarios. <b>Methods</b>: Using data from 207 breast cancer patients treated with RT, we developed and tested ML models at three MHD cut-off values (240, 270, and 300 cGy), considering variations in the number of independent variables (three vs. six) and folds in the cross-validation (three, four, and five). Robustness was defined as achieving high F2 scores and low instability in predictive performance. <b>Results</b>: Our findings indicate that the decision tree (DT) model demonstrated consistently high robustness at 240 and 270 cGy, while the random forest model performed optimally at 300 cGy. At 240 cGy, a threshold critical to minimize late cardiac risks, the DT model exhibited stable predictive power, reducing the risk of overestimating DIBH necessity. <b>Conclusions</b>: These results suggest that the DT model, particularly at lower MHD thresholds, may be the most reliable for clinical applications. By providing a tool for targeted DIBH implementation, this model has the potential to enhance patient-specific treatment planning and improve clinical outcomes in RT. |
| format | Article |
| id | doaj-art-d39322ae127e446fa6b4d1d8cb0e4b03 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-d39322ae127e446fa6b4d1d8cb0e4b032025-08-20T03:43:36ZengMDPI AGDiagnostics2075-44182025-03-0115666810.3390/diagnostics15060668Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation TherapyWlla E. Al-Hammad0Masahiro Kuroda1Ghaida Al Jamal2Mamiko Fujikura3Ryo Kamizaki4Kazuhiro Kuroda5Suzuka Yoshida6Yoshihide Nakamura7Masataka Oita8Yoshinori Tanabe9Kohei Sugimoto10Irfan Sugianto11Majd Barham12Nouha Tekiki13Miki Hisatomi14Junichi Asaumi15Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, JapanRadiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, JapanDepartment of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, JordanDepartment of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, JapanRadiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, JapanRadiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, JapanDepartment of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, JapanDepartment of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, JapanGraduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama 770-8558, JapanRadiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, JapanRadiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, JapanDepartment of Oral Radiology, Faculty of Dentistry, Hasanuddin University, Sulawesi 90245, IndonesiaDepartment of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University, Nablus 44839, PalestineDepartment of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, JapanDepartment of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, JapanDepartment of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan<b>Background/Objectives:</b> Deep inspiration breath-hold (DIBH) is a commonly used technique to reduce the mean heart dose (MHD), which is critical for minimizing late cardiac side effects in breast cancer patients undergoing radiation therapy (RT). Although previous studies have explored the potential of machine learning (ML) to predict which patients might benefit from DIBH, none have rigorously assessed ML model performance across various MHD thresholds and parameter settings. This study aims to evaluate the robustness of ML models in predicting the need for DIBH across different clinical scenarios. <b>Methods</b>: Using data from 207 breast cancer patients treated with RT, we developed and tested ML models at three MHD cut-off values (240, 270, and 300 cGy), considering variations in the number of independent variables (three vs. six) and folds in the cross-validation (three, four, and five). Robustness was defined as achieving high F2 scores and low instability in predictive performance. <b>Results</b>: Our findings indicate that the decision tree (DT) model demonstrated consistently high robustness at 240 and 270 cGy, while the random forest model performed optimally at 300 cGy. At 240 cGy, a threshold critical to minimize late cardiac risks, the DT model exhibited stable predictive power, reducing the risk of overestimating DIBH necessity. <b>Conclusions</b>: These results suggest that the DT model, particularly at lower MHD thresholds, may be the most reliable for clinical applications. By providing a tool for targeted DIBH implementation, this model has the potential to enhance patient-specific treatment planning and improve clinical outcomes in RT.https://www.mdpi.com/2075-4418/15/6/668breast cancerradiation therapyheart dosecut-off valuemachine learningrobustness |
| spellingShingle | Wlla E. Al-Hammad Masahiro Kuroda Ghaida Al Jamal Mamiko Fujikura Ryo Kamizaki Kazuhiro Kuroda Suzuka Yoshida Yoshihide Nakamura Masataka Oita Yoshinori Tanabe Kohei Sugimoto Irfan Sugianto Majd Barham Nouha Tekiki Miki Hisatomi Junichi Asaumi Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy Diagnostics breast cancer radiation therapy heart dose cut-off value machine learning robustness |
| title | Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy |
| title_full | Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy |
| title_fullStr | Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy |
| title_full_unstemmed | Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy |
| title_short | Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy |
| title_sort | robustness of machine learning predictions for determining whether deep inspiration breath hold is required in breast cancer radiation therapy |
| topic | breast cancer radiation therapy heart dose cut-off value machine learning robustness |
| url | https://www.mdpi.com/2075-4418/15/6/668 |
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