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

被引:2
作者
Kamizaki, Ryo [1 ,2 ]
Kuroda, Masahiro [1 ,13 ]
Al-Hammad, Wlla E. [3 ,4 ]
Tekiki, Nouha [3 ]
Ishizaka, Hinata [1 ]
Kuroda, Kazuhiro [1 ,5 ]
Sugimoto, Kohei [1 ,6 ]
Oita, Masataka [6 ]
Tanabe, Yoshinori [1 ]
Barham, Majd [7 ]
Sugianto, Irfan [8 ]
Nakamitsu, Yuki [1 ]
Hirano, Masaki [1 ,9 ]
Muto, Yuki [1 ,10 ]
Ihara, Hiroki [11 ]
Sugiyama, Soichi [12 ]
机构
[1] Okayama Univ, Grad Sch Hlth Sci, Dept Radiol Technol, Okayama 7008558, Japan
[2] Matsuyama Red Cross Hosp, Dept Radiol, Matsuyama, Ehime 7908524, Japan
[3] Okayama Univ, Dept Oral & Maxillofacial Radiol, Dent & Pharmaceut Sci, Grad Sch Med, Okayama 7008558, Japan
[4] Jordan Univ Sci & Technol, Fac Dent, Dept Oral Med & Oral Surg, Irbid 22110, Jordan
[5] Okayama Prefectural Univ, Grad Sch Hlth & Welf Sci, Dept Hlth & Welf Sci, Okayama 7191197, Japan
[6] Okayama Univ, Grad Sch Interdisciplinary Sci & Engn Hlth Syst, Okayama 7708558, Japan
[7] Annajah Natl Univ, Coll Med & Hlth Sci, Dept Dent & Dent Surg, Nablus 44839, Palestine
[8] Hasanuddin Univ, Fac Dent, Dept Oral Radiol, Sulawesi 90245, Indonesia
[9] Osaka Red Cross Hosp, Dept Radiol, Osaka 5438555, Japan
[10] Oomoto Hosp, Dept Radiol, Okayama 7000924, Japan
[11] Okayama Univ, Fac Med Dent & Pharmaceut Sci, Dept Radiol, Okayama 7008558, Japan
[12] Okayama Univ, Grad Sch Med Dent & Pharmaceut Sci, Dept Proton Beam Therapy, Okayama 7008558, Japan
[13] Okayama Univ, Grad Sch Hlth Sci, Dept Radiol Technol, 2-5-1 Shikata Cho,Kita Ku, Okayama 7008558, Japan
关键词
BC; RT; heart dose; ML; DNN; DIBH; VOLUME;
D O I
10.3892/etm.2023.12235
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
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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页数:8
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