Saiyo Nipon, Assawanuwat Kritsrun, Janthawanno Patthra, Paduka Sumana, Prempetch Kantamanee, Chanphol Thammasak, Sakchatchawan Bualookkaew, Thongsawad Sangutid
School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand.
Department of Radiation Oncology, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok, Thailand.
Radiol Phys Technol. 2025 Jun 2. doi: 10.1007/s12194-025-00916-z.
This study aimed to develop a model for predicting the bladder volume ratio between daily CBCT and CT to determine adequate bladder filling in patients undergoing treatment for prostate cancer with external beam radiation therapy (EBRT). The model was trained using 465 datasets obtained from 34 prostate cancer patients. A total of 16 features were collected as input data, which included basic patient information, patient health status, blood examination laboratory results, and specific radiation therapy information. The ratio of the bladder volume between daily CBCT (dCBCT) and planning CT (pCT) was used as the model response. The model was trained using a bootstrap aggregation (bagging) algorithm with two machine learning (ML) approaches: classification and regression. The model accuracy was validated using other 93 datasets. For the regression approach, the accuracy of the model was evaluated based on the root mean square error (RMSE) and mean absolute error (MAE). By contrast, the model performance of the classification approach was assessed using sensitivity, specificity, and accuracy scores. The ML model showed promising results in the prediction of the bladder volume ratio between dCBCT and pCT, with an RMSE of 0.244 and MAE of 0.172 for the regression approach, sensitivity of 95.24%, specificity of 92.16%, and accuracy of 93.55% for the classification approach. The prediction model could potentially help the radiological technologist determine whether the bladder is full before treatment, thereby reducing the requirement for re-scan CBCT. HIGHLIGHTS: The bagging model demonstrates strong performance in predicting optimal bladder filling. The model achieves promising results with 95.24% sensitivity and 92.16% specificity. It supports therapists in assessing bladder fullness prior to treatment. It helps reduce the risk of requiring repeat CBCT scans.
本研究旨在开发一种模型，用于预测每日锥形束计算机断层扫描（CBCT）与计算机断层扫描（CT）之间的膀胱体积比，以确定接受外照射放疗（EBRT）治疗前列腺癌患者的膀胱充盈是否充足。该模型使用从34例前列腺癌患者获得的465个数据集进行训练。总共收集了16个特征作为输入数据，包括患者基本信息、患者健康状况、血液检查实验室结果以及特定放疗信息。每日CBCT（dCBCT）与计划CT（pCT）之间的膀胱体积比用作模型响应。该模型使用带有两种机器学习（ML）方法的自助聚合（装袋）算法进行训练：分类和回归。使用其他93个数据集验证模型准确性。对于回归方法，基于均方根误差（RMSE）和平均绝对误差（MAE）评估模型准确性。相比之下，使用敏感性、特异性和准确性得分评估分类方法的模型性能。该ML模型在预测dCBCT和pCT之间的膀胱体积比方面显示出有前景的结果，回归方法的RMSE为0.244，MAE为0.172，分类方法的敏感性为95.24%，特异性为92.16%，准确性为93.55%。该预测模型可能有助于放射技师在治疗前确定膀胱是否充盈，从而减少重复扫描CBCT的需求。要点：装袋模型在预测最佳膀胱充盈方面表现出强大性能。该模型取得了有前景的结果，敏感性为95.24%，特异性为92.16%。它支持治疗师在治疗前评估膀胱充盈情况。它有助于降低需要重复进行CBCT扫描的风险。
