Kamizaki Ryo, Kuroda Masahiro, Al-Hammad Wlla E, Tekiki Nouha, Ishizaka Hinata, Kuroda Kazuhiro, Sugimoto Kohei, Oita Masataka, Tanabe Yoshinori, Barham Majd, Sugianto Irfan, Nakamitsu Yuki, Hirano Masaki, Muto Yuki, Ihara Hiroki, Sugiyama Soichi
Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Department of Radiology, Matsuyama Red Cross Hospital, Matsuyama, Ehime 790-8524, Japan.
Exp Ther Med. 2023 Oct 2;26(5):536. doi: 10.3892/etm.2023.12235. eCollection 2023 Nov.
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.
左侧乳腺癌(BC)术后放疗(RT)期间心脏剂量增加会导致心脏损伤,进而降低患者生存率。深吸气屏气技术(DIBH)在降低左侧BC患者的平均心脏剂量(MHD)方面越来越普遍。然而,RT的治疗计划和DIBH对患者和RT工作人员来说既费力、耗时又昂贵。此外,亚洲女性中左侧BC且MHD较低的患者比例相当高,主要是因为与西方国家相比,她们的乳房体积较小。本研究旨在确定最佳机器学习(ML)模型,以预测RT后的MHD,从而在RT计划前预先选择MHD较低且不需要DIBH的患者。总共562例接受术后RT的BC患者使用Python(版本3.8)随机分为用于ML的训练验证集(n = 449)和外部测试集(n = 113)。使用带高斯噪声的合成少数过采样来校正不平衡数据。具体而言,左右、肿瘤部位、胸壁厚度、照射方法、体重指数和间距是用于ML的六个解释变量,使用了四种监督ML算法。以均方根误差(RMSE)作为内部测试数据的指标,使用超参数调整的最佳值,选择F2评分评估最佳的模型用于外部测试数据的最终验证。对于截止值为300 cGy,在所有算法中,深度神经网络对RT后真实MHD的MHD预测能力最高,RMSE为77.4,F2评分为0.80,曲线下面积-接收器操作特征为0.88。本研究表明,ML可用于预先选择MHD较低且不需要DIBH进行BC术后RT的亚洲女性患者。