Thropp Connor, Hepel Jaroslaw, Leech Timothy, Klein Eric E, Li Qiongge
Department of Medical Physics, Brown University, Providence, Rhode Island.
Department of Radiation Oncology at Rhode Island Hospital, Providence, Rhode Island.
Adv Radiat Oncol. 2025 May 10;10(7):101804. doi: 10.1016/j.adro.2025.101804. eCollection 2025 Jul.
Erroneous radiation therapy (RT) prescriptions (Rx) can lead to injury or death of patients. A novel data-driven model that uses similarity learning to identify atypical Rx was recently published. In that study, prototype analysis was conducted within a single institution with a single treatment site. The present study sets out to validate the robustness of the model by applying the model to multiple disease sites using a different institution's data.
A query was conducted of Brown University Health RT treatment records for thoracic and brain cancer patients from 1995 to 2021 to create historical databases used for training. The query included records containing data on the Rx and patient-specific features. Simulated anomalies were created to mimic potential errors and were used in the training and testing of the model. Model performance was evaluated using F1 score.
F1 scores for the brain site are 99% for intensity modulated RT, 90% for stereotactic radiation therapy/ radiosurgery/SRT, and 94% for 3-dimensional RT. F1 scores for the thoracic site are 95%, 90%, and 95% for the 3 techniques, respectively. Statistical analysis shows no significant differences between the model's prediction and ground truth.
The model performance shows feasibility for application to various disease sites across different institutions. This model can be used alongside physicians and physicists during peer review chart rounds to aid in the detection of potential RT Rx errors.
错误的放射治疗（RT）处方可能导致患者受伤或死亡。最近发表了一种使用相似性学习来识别非典型处方的新型数据驱动模型。在该研究中，在单一机构的单一治疗部位进行了原型分析。本研究旨在通过使用不同机构的数据将该模型应用于多个疾病部位来验证模型的稳健性。
对布朗大学健康放射治疗中心1995年至2021年期间胸癌和脑癌患者的治疗记录进行查询，以创建用于训练的历史数据库。查询包括包含处方和患者特定特征数据的记录。创建模拟异常以模拟潜在错误，并将其用于模型的训练和测试。使用F1分数评估模型性能。
脑部的F1分数在调强放疗中为99%，在立体定向放射治疗/放射外科手术/SRT中为90%，在三维放疗中为94%。胸部的F1分数在这三种技术中分别为95%、90%和95%。统计分析表明模型预测与实际情况之间无显著差异。
模型性能表明该模型可应用于不同机构的各种疾病部位。该模型可在同行评审图表轮次中与医生和物理学家一起使用，以帮助检测潜在的放射治疗处方错误。
