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ID 66018
フルテキストURL
著者
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 ORCID Kaken ID publons researchmap
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 Kaken ID researchmap
Tanabe, Yoshinori Department of Radiological Technology, Graduate School of Health Sciences, Okayama University ORCID Kaken ID researchmap
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
抄録
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.
キーワード
BC
RT
heart dose
ML
DNN
DIBH
備考
This fulltext file will be available in Apr. 2024.
発行日
2023-10-02
出版物タイトル
Experimental and Therapeutic Medicine
26巻
5号
出版者
Spandidos Publications
開始ページ
536
ISSN
1792-0981
資料タイプ
学術雑誌論文
言語
英語
OAI-PMH Set
岡山大学
著作権者
© Spandidos Publications 2023.
論文のバージョン
publisher
PubMed ID
DOI
Web of Science KeyUT
関連URL
isVersionOf https://doi.org/10.3892/etm.2023.12235
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
助成機関名
Ministry of Health, Labour and Welfare of Japan
助成番号
23K07063