A planning quality evaluation tool for prostate adaptive IMRT based on machine learning
A planning quality evaluation tool for prostate adaptive IMRT based on machine learning
复制标题
DOI：
10.1118/1.3539749
复制
发表时间：
2011-02-01
期刊：
影响因子：
3.8
通讯作者：
Wu, Q. Jackie
中科院分区：
文献类型：
--
作者：
Zhu, Xiaofeng;Ge, Yaorong;Wu, Q. Jackie
目的：为了确保前列腺自适应调强放射治疗的计划质量，我们使用机器学习方法开发了一种定量评估工具。该工具生成的剂量体积直方图（DVH）的器官在危险（OARs）的基础上作为参考，与来自注量图deformation.Methods的自适应计划相比：在相同的配置下，使用7场15 MV光子束，DVH的OARs（膀胱和直肠）估计基于患者的解剖信息和模型从数据库中学习的高质量的事先计划。在这项研究中，解剖信息的特点是器官体积和距离目标直方图（DTH）。该数据库由198个高质量的前列腺计划组成，并在训练库之外的14个病例中进行了验证。主成分分析（PCA）应用于DVH和DTH量化其显着特征。结果：DVH/DTH曲线仅用2 ~ 3个截断主成分就能充分表征，从而减少了变量数目，量化了患者的解剖信息。使用测试数据集的模型的评估表明，其准确性接近80%的预测和有效性，在提高ART planning quality.Conclusions：一个自适应IMRT计划质量评估工具的基础上，机器学习已经开发，估计OAR的保留，并提供参考评估ART。（c）2011年美国医学物理学家协会。[DOI：10.1118/1.3539749]
Purpose: To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation.Methods: Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information.Results: DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy similar to 80% in prediction and effectiveness in improving ART planning quality.Conclusions: An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART. (c) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3539749]