(Thursday, 7/18/2019) 7:30 AM - 9:30 AM
Room: Stars at Night Ballroom 2-3
Purpose: To estimate patient-specific PTV margins for liver SBRT with motion-tracking based on a dry-run session prior to planning (mock treatment), and provide warnings when relatively large errors are predicted during treatment.
Methods: Using log file data for 118 courses (104 patients), 22 features from their first Synchrony model (emulating the mock treatment) including motion-compensation errors are calculated. A support vector machine (SVM) classifier is trained using mock data to predict root-mean-square errors for the subsequent treatment models to facilitate patient-specific margins. For the warning system, the only data used is what is available to the CyberKnife Synchrony system up until the most recent X-ray image is acquired. These data include all manual user interactions such as removing invalid model points. 56 features are calculated including statistics concerning morphological LED time-trace features. A warning system is created via a linear SVM classifier, trained with mock data and X-ray data to predict high errors (≥ 3 mm) during the treatment. The classifiers are evaluated by confusion matrix and receiver operating characteristic metric.
Results: The mock model gives an accuracy of 89% ± 9% (5-fold cross-validation) to predict if root-mean-square errors for subsequent treatments are above 2 mm for all courses. The variation of errors, linearity between internal target motion and external marker motion, and target motion amplitudes from mock data are important for classifying two groups (errors ≥ 2 mm and < 2 mm). For 84% of the time, the warning system can give warnings of an incoming error above 3 mm evaluated via group 5-fold cross-validation.
Conclusion: The mock data can be used to predict errors to provide patient-specific margins prior to planning. It is possible to build a warning system to predict when errors above a predefined threshold will occur during the treatment.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the Ontario Consortium for Adaptive Interventions Radiation Oncology, a research grant from Accuray Inc., CA, Ontario Graduate Scholarship, and John Lyndhurst Kingston Scholarship.
Keywords
Statistical Analysis, Stereotactic Radiosurgery, Respiration
Taxonomy
TH- Dataset analysis/biomathematics: Machine learning techniques
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