通过机器学习评估心脏剂量预测的准确性,以选择癌症手术后不需要深吸气屏息放疗的患者。

Experimental and therapeutic medicine Pub Date : 2023-10-02 eCollection Date: 2023-11-01 DOI:10.3892/etm.2023.12235
Ryo Kamizaki, Masahiro Kuroda, Wlla E Al-Hammad, Nouha Tekiki, Hinata Ishizaka, Kazuhiro Kuroda, Kohei Sugimoto, Masataka Oita, Yoshinori Tanabe, Majd Barham, Irfan Sugianto, Yuki Nakamitsu, Masaki Hirano, Yuki Muto, Hiroki Ihara, Soichi Sugiyama
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引用次数: 0

摘要

左侧癌症(BC)术后放疗(RT)期间心脏剂量增加会导致心脏损伤,从而降低患者生存率。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工作人员来说是费力、耗时和昂贵的。此外,亚洲女性中左乳腺癌患者MHD低的比例要高得多,这主要是因为与西方国家相比,她们的乳房体积较小。本研究旨在确定预测RT后MHD的最佳机器学习(ML)模型,以预先选择在RT计划前不需要DIBH的低MHD患者。总共,562名接受术后RT的BC患者被随机分为训练(n=449)和外部(n=113)测试数据集,使用Python(3.8版)进行ML。使用高斯噪声的合成少数过采样校正不平衡数据。具体而言,左右、肿瘤部位、胸壁厚度、照射方法、体重指数和分离度是ML的六个解释变量,使用了四种监督ML算法。使用具有均方根误差(RMSE)的超参数调整的最佳值作为内部测试数据的指标,选择产生最佳F2分数评估的模型,使用外部测试数据进行最终验证。在深度神经网络的所有算法中,MHD对RT后真实MHD的预测能力最高,RMSE为77.4,F2得分为0.80,曲线下面积接收器操作特性为0.88,截止值为300 cGy。本研究表明,ML可用于预选MHD低的亚洲女性患者,这些患者不需要DIBH进行BC的术后RT。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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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