ID | 66018 |
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Author |
Kamizaki, Ryo
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University
Kuroda, Masahiro
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University
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Kaken ID
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Al‑Hammad, Wlla
Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
Tekiki, Nouha
Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
Ishizaka, Hinata
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University
Kuroda, Kazuhiro
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University
Sugimoto, Kohei
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University
Oita, Masataka
Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University
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Tanabe, Yoshinori
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University
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Barham, Majd
Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An‑Najah National University
Sugianto, Irfan
Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University
Nakamitsu, Yuki
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University
Hirano, Masaki
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University
Muto, Yuki
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University
Ihara, Hiroki
Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
Sugiyama, Soichi
Department of Proton Beam Therapy, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Abstract | Increased heart dose during postoperative radiotherapy (RT) for left‑sided breast cancer (BC) can cause cardiac injury, which can decrease patient survival. The deep inspiration breath‑hold technique (DIBH) is becoming increasingly common for reducing the mean heart dose (MHD) in patients with left‑sided BC. However, treatment planning and DIBH for RT are laborious, time‑consuming and costly for patients and RT staff. In addition, the proportion of patients with left BC with low MHD is considerably higher among Asian women, mainly due to their smaller breast volume compared with that in Western countries. The present study aimed to determine the optimal machine learning (ML) model for predicting the MHD after RT to pre‑select patients with low MHD who will not require DIBH prior to RT planning. In total, 562 patients with BC who received postoperative RT were randomly divided into the trainval (n=449) and external (n=113) test datasets for ML using Python (version 3.8). Imbalanced data were corrected using synthetic minority oversampling with Gaussian noise. Specifically, right‑left, tumor site, chest wall thickness, irradiation method, body mass index and separation were the six explanatory variables used for ML, with four supervised ML algorithms used. Using the optimal value of hyperparameter tuning with root mean squared error (RMSE) as an indicator for the internal test data, the model yielding the best F2 score evaluation was selected for final validation using the external test data. The predictive ability of MHD for true MHD after RT was the highest among all algorithms for the deep neural network, with a RMSE of 77.4, F2 score of 0.80 and area under the curve‑receiver operating characteristic of 0.88, for a cut‑off value of 300 cGy. The present study suggested that ML can be used to pre‑select female Asian patients with low MHD who do not require DIBH for the postoperative RT of BC.
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Keywords | BC
RT
heart dose
ML
DNN
DIBH
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Note | This fulltext file will be available in Apr. 2024.
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Published Date | 2023-10-02
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Publication Title |
Experimental and Therapeutic Medicine
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Volume | volume26
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Issue | issue5
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Publisher | Spandidos Publications
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Start Page | 536
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ISSN | 1792-0981
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Content Type |
Journal Article
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language |
English
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OAI-PMH Set |
岡山大学
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Copyright Holders | © Spandidos Publications 2023.
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File Version | publisher
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PubMed ID | |
DOI | |
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Related Url | isVersionOf https://doi.org/10.3892/etm.2023.12235
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Citation | Kamizaki R, Kuroda M, Al‑Hammad WE, Tekiki N, Ishizaka H, Kuroda K, Sugimoto K, Oita M, Tanabe Y, Barham M, Barham M, et al: Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath‑hold radiotherapy after breast cancer surgery. Exp Ther Med 26: 536, 2023
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Funder Name |
Ministry of Health, Labour and Welfare of Japan
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助成番号 | 23K07063
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