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

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.