Mitigating Errors in External Respiratory Surrogate-Based Models of Tumor Position
Mitigating Errors in External Respiratory Surrogate-Based Models of Tumor Position
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DOI：
10.1016/j.ijrobp.2011.05.042
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发表时间：
2012-04-01
影响因子：
7
通讯作者：
D'Souza, Warren D.
中科院分区：
文献类型：
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作者：
Malinowski, Kathleen T.;McAvoy, Thomas J.;D'Souza, Warren D.
目的：为了研究肿瘤部位，测量精度，肿瘤替代相关性，训练数据选择，模型设计，和患者间和interfraction变化的准确性外部标记为基础的模型的肿瘤position.Methods和材料：射波刀同步系统日志文件，包括同步采集的外部标记和肿瘤的位置从167治疗分数进行了分析。评价了同步、普通最小二乘回归和偏最小二乘回归模型用于从外部标志物预测肿瘤位置的准确性。用于建立预测模型的数据的数量和时间是不同的。肿瘤替代物的相关性和精确度的影响，在肿瘤和外部替代位置测量进行了探讨，通过添加噪声的data.Results：肿瘤位置预测误差增加的持续时间的一部分。增加训练数据量并不总是导致更准确的模型。将不相关的噪声添加到基于外部标记物的输入中，使偏最小二乘法的tumorescrogate相关模型降低了16%，普通最小二乘法降低了57%。外部标记物和肿瘤位置测量误差导致肿瘤位置预测变化为测量误差幅度的0.3-3.6倍，随模型算法变化很大。肿瘤位置预测误差与患者指数显著相关，但与分数指数或肿瘤部位无关。偏最小二乘法是准确的同步和更准确的比普通least squares.Conclusions：替代为基础的推断模型的肿瘤位置的准确性受到所有的调查因素，除了肿瘤部位和分数指数。(C)2012 Elsevier Inc.
Purpose: To investigate the effect of tumor site, measurement precision, tumor-surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position.Methods and Materials: Cyberknife Synchrony system log files comprising synchronously acquired positions of external markers and the tumor from 167 treatment fractions were analyzed. The accuracy of Synchrony, ordinary-least-squares regression, and partial-least-squares regression models for predicting the tumor position from the external markers was evaluated. The quantity and timing of the data used to build the predictive model were varied. The effects of tumoresurrogate correlation and the precision in both the tumor and the external surrogate position measurements were explored by adding noise to the data.Results: The tumor position prediction errors increased during the duration of a fraction. Increasing the training data quantities did not always lead to more accurate models. Adding uncorrelated noise to the external marker-based inputs degraded the tumoresurrogate correlation models by 16% for partial-least-squares and 57% for ordinary-least-squares. External marker and tumor position measurement errors led to tumor position prediction changes 0.3-3.6 times the magnitude of the measurement errors, varying widely with model algorithm. The tumor position prediction errors were significantly associated with the patient index but not with the fraction index or tumor site. Partial-least-squares was as accurate as Synchrony and more accurate than ordinary-least-squares.Conclusions: The accuracy of surrogate-based inferential models of tumor position was affected by all the investigated factors, except for the tumor site and fraction index. (C) 2012 Elsevier Inc.