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タイトル
  • en 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
作成者
    • en Kamizaki, Ryo
    • en Kuroda, Masahiro
    • en Al‑Hammad, Wlla
    • en Tekiki, Nouha
    • en Ishizaka, Hinata
    • en Kuroda, Kazuhiro
    • en Sugimoto, Kohei
    • en Oita, Masataka
    • en Tanabe, Yoshinori
    • en Barham, Majd
    • en Sugianto, Irfan
    • en Nakamitsu, Yuki
    • en Hirano, Masaki
    • en Muto, Yuki
    • en Ihara, Hiroki
    • en Sugiyama, Soichi
権利情報
  • © Spandidos Publications 2023.
主題
  • Other BC
  • Other RT
  • Other heart dose
  • Other ML
  • Other DNN
  • Other DIBH
内容注記
  • Other 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.
出版者 en Spandidos Publications
日付
    Issued2023-10-02
言語
  • eng
資源タイプ journal article
出版タイプ VoR
資源識別子 URI https://ousar.lib.okayama-u.ac.jp/66018
関連
  • isIdenticalTo PMID 37869640
  • isIdenticalTo DOI https://doi.org/10.3892/etm.2023.12235
収録誌情報
    • ISSN 1792-0981
      • Experimental and Therapeutic Medicine
      • 巻26 号5 開始ページ536
ファイル
コンテンツ更新日時 2024-04-05