Program Information
A Unified Machine-Learning Based Probabilistic Model for Automated Anomaly Detection in the Treatment Plan Data
X Chang1*, A Kalet2 , S Liu3 , D Yang4 , (1) Washington University in St. Louis, St. Louis, MO, (2) University of Washington Medical Center, Seattle, WA, (3) Washington University in St. Louis, St. Louis, MO, (4) Washington University in St Louis, St Louis, MO
Presentations
WE-H-BRC-6 (Wednesday, August 3, 2016) 4:30 PM - 6:00 PM Room: Ballroom C
Purpose:The purpose of this work was to investigate the ability of a machine-learning based probabilistic approach to detect radiotherapy treatment plan anomalies given initial disease classes information.
Methods:In total we obtained 1112 unique treatment plans with five plan parameters and disease information from a Mosaiq treatment management system database for use in the study. The plan parameters include prescription dose, fractions, fields, modality and techniques. The disease information includes disease site, and T, M and N disease stages. A Bayesian network method was employed to model the probabilistic relationships between tumor disease information, plan parameters and an anomaly flag. A Bayesian learning method with Dirichlet prior was useed to learn the joint probabilities between dependent variables in error-free plan data and data with artificially induced anomalies. In the study, we randomly sampled data with anomaly in a specified anomaly space.We tested the approach with three groups of plan anomalies – improper concurrence of values of all five plan parameters and values of any two out of five parameters, and all single plan parameter value anomalies. Totally, 16 types of plan anomalies were covered by the study. For each type, we trained an individual Bayesian network.
Results:We found that the true positive rate (recall) and positive predictive value (precision) to detect concurrence anomalies of five plan parameters in new patient cases were 94.45±0.26% and 93.76±0.39% respectively. To detect other 15 types of plan anomalies, the average recall and precision were 93.61±2.57% and 93.78±3.54% respectively. The computation time to detect the plan anomaly of each type in a new plan is ~0.08 seconds.
Conclusion:The proposed method for treatment plan anomaly detection was found effective in the initial tests. The results suggest that this type of models could be applied to develop plan anomaly detection tools to assist manual and automated plan checks.
Funding Support, Disclosures, and Conflict of Interest: The senior author received research grants from ViewRay Inc. and Varian Medical System.
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