Online monitoring and error detection of real‐time tumor displacement prediction accuracy using control limits on respiratory surrogate statistics

Medical Physics - Tập 39 Số 4 - Trang 2042-2048 - 2012
K Malinowski1, Thomas J. McAvoy2, Rohini George3, Sonja Dieterich4, W DˈSouza5
1Fischell Department of Bioengineering, University of Maryland - College Park, MD, USA
2University of Maryland, College Park, College Park, United States
3Department of Radiation Oncology; University of Maryland School of Medicine; Baltimore MD 21201
4Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California 94305
5Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201 and Fischell Department of Bioengineering, A. James Clark School of Engineering, University of Maryland, College Park, Maryland 20742

Tóm tắt

Purpose:To evaluate Hotelling'sT2 statistic and the input variable squared prediction error (Q(X)) for detecting large respiratory surrogate‐based tumor displacement prediction errors without directly measuring the tumor's position.Methods:Tumor and external marker positions from a database of 188 Cyberknife Synchrony™ lung, liver, and pancreas treatment fractions were analyzed. The first ten measurements of tumor position in each fraction were used to create fraction‐specific models of tumor displacement using external surrogates as input; the models were used to predict tumor position from subsequent external marker measurements. A partial least squares (PLS) model with four scores was developed for each fraction to determineT2 and Q(X) confidence limits based on the first ten measurements in a fraction. The T2 and Q(X) statistics were then calculated for every set of external marker measurements. Correlations between model error and both T2 and Q(X) were determined. Receiver operating characteristic analysis was applied to evaluate sensitivities and specificities of T2, Q(X), and T2∪Q(X) for predicting real‐time tumor localization errors >3 mm over a range of T2 and Q(X) confidence limits.Results:Sensitivity and specificity of detecting errors >3 mm varied with confidence limit selection. At 95% sensitivity,T2∪Q(X) specificity was 15%, 2% higher than either T2 or Q(X) alone. The mean time to alarm for T2∪Q(X) at 95% sensitivity was 5.3 min but varied with a standard deviation of 8.2 min. Results did not differ significantly by tumor site.Conclusions:The results of this study establish the feasibility of respiratory surrogate‐based online monitoring of real‐time respiration‐induced tumor motion model accuracy for lung, liver, and pancreas tumors. TheT2 and Q(X) statistics were able to indicate whether inferential model errors exceeded 3 mm with high sensitivity. Modest improvements in specificity were achieved by combining T2 and Q(X) results.

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Thông tin xuất bản

Medical Physics

Tập 39 Số 4

2042-2048

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