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• W2018153104 abstract "Real-time motion management is important in radiotherapy. In addition to effective monitoring schemes, prediction is required to compensate for system latency, so that treatment can be synchronized with tumor motion. However, it is difficult to predict tumor motion at all times, and it is critical to determine when large prediction errors may occur. Such information can be used to pause the treatment beam or adjust monitoring/prediction schemes. In this study, we propose a hypothesis testing approach for detecting instants corresponding to potentially large prediction errors in real time. We treat the future tumor location as a random variable, and obtain its empirical probability distribution with the kernel density estimation-based method. Under the null hypothesis, the model probability is assumed to be a concentrated Gaussian centered at the prediction output. Under the alternative hypothesis, the model distribution is assumed to be non-informative uniform, which reflects the situation that the future position cannot be inferred reliably. We derive the likelihood ratio test (LRT) for this hypothesis testing problem and show that with the method of moments for estimating the null hypothesis Gaussian parameters, the LRT reduces to a simple test on the empirical variance of the predictive random variable. This conforms to the intuition to expect a (potentially) large prediction error when the estimate is associated with high uncertainty, and to expect an accurate prediction when the uncertainty level is low. We tested the proposed method on patient-derived respiratory traces. The 'ground-truth' prediction error was evaluated by comparing the prediction values with retrospective observations, and the large prediction regions were subsequently delineated by thresholding the prediction errors. The receiver operating characteristic curve was used to describe the performance of the proposed hypothesis testing method. Clinical implication was represented by miss detection rate and delivery efficiency. Both characterizations demonstrated the promising results and provided insight into the tradeoffs in the detection task. This study opens the discussion on real-time analysis of prediction accuracy and promises important information in automatically adjusting treatment and/or target monitoring schemes." @default.
• W2018153104 created "2016-06-24" @default.
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• W2018153104 date "2010-06-22" @default.
• W2018153104 modified "2023-09-26" @default.
• W2018153104 title "Prospective detection of large prediction errors: a hypothesis testing approach" @default.
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• W2018153104 doi "https://doi.org/10.1088/0031-9155/55/13/021" @default.
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