早期非小细胞肺癌立体定向放疗肺剂量预测研究

目的 研究基于机器学习算法的早期非小细胞肺癌立体定向放疗肺剂量预测方法和应用于计划质量控制的可行性。方法 利用机器学习算法实现剂量预测。首先,建立专家计划库,提取计划库中的几何特征信息、照射野角度和剂量体积直方图(DVH)参数,在几何及照射野特征和DVH之间建立相关模型;其次,提取专家库外10例患者的几何和照射野特征信息,利用模型预测可实现的DVH值,并将其与实际计划结果比较。结果 10例患者肺平均剂量和V20外部验证的均方根误差分别为91.95 cGy和3.12%。对肺受量高于预测剂量的2例计划进行修改,修改后肺剂量均有所降低。结论 对非小细胞肺癌患者制定立体定向放疗计划前,可根据相关数学模型提前预测肺DVH曲线作为计划评估标准,从而保证治疗计划的质量。

A study of prediction model of lung dose in early stage non-small cell lung cancer with stereotactic body radiotherapy

Objective To study a lung dose prediction method for the early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy based on machine learning algorithm,and to evaluate the feasibility of application in planning quality assurance. Methods A machine learning algorithm was utilized to achieve DVH prediction. First, an expert plan dataset with 125 cases was built,and the geometric features of ROI,beam angle anddose-volume histogram(DVH) parameters in the dataset were extracted. Following a correlation model was established between the features and DVHs. Second,the geometric and beam features from 10 cases outside the training pool were extracted,and the model was adopted to predict the achievable DVHs values of the lung. The predicted DVHs values were compared with the actual planned results. Results The mean squared errors of external validation for the 10 cases inmean lung dose (MLD)MLD and V20 of the lung were 91.95 cGy and 3.12%,respectively. Two cases whose lungdoseswere higher than the predicted values were re-planned,and the results showed that the the lung doses were reduced. Conclusion It is feasible to utilize the anatomy and beam angle features to predict the lung DVH parameters for plan evaluation and quality assurance in early stage NSCLC patients treated with stereotactic body radiotherapy