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Online monitoring and error detection of real‐time tumor displacement prediction accuracy using control limits on respiratory surrogate statistics
Author(s) -
Malinowski Kathleen,
McAvoy Thomas J.,
George Rohini,
Dieterich Sonja,
D'Souza Warren D.
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3676690
Subject(s) - confidence interval , sensitivity (control systems) , statistics , receiver operating characteristic , mathematics , nuclear medicine , standard deviation , position (finance) , algorithm , medicine , electronic engineering , engineering , finance , economics
Purpose: To evaluate Hotelling's T 2 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 determine T 2 and Q ( X ) confidence limits based on the first ten measurements in a fraction. The T 2 and Q ( X ) statistics were then calculated for every set of external marker measurements. Correlations between model error and both T 2 and Q ( X ) were determined. Receiver operating characteristic analysis was applied to evaluate sensitivities and specificities of T 2 , Q ( X ) , and T 2 ∪ Q ( X ) for predicting real‐time tumor localization errors >3 mm over a range of T 2 and Q ( X ) confidence limits.Results: Sensitivity and specificity of detecting errors >3 mm varied with confidence limit selection. At 95% sensitivity, T 2 ∪ Q ( X ) specificity was 15%, 2% higher than either T 2 or Q ( X ) alone. The mean time to alarm for T 2 ∪ 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. The T 2 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 T 2 and Q ( X ) results.