Efficiently train and validate a RapidPlan model through <scp>APQM</scp> scoring

Abstract

PURPOSE: The aim of this study was to propose and validate an intuitive method for training and to validate knowledge-based planning (KBP) systems based on a patient-specific plan quality scoring. METHODS: models. No further refinements or actions were undertaken on these two models. Their performances were benchmarked against another two RapidPlan models. An Uncleaned model, which was populated and trained with the initial sample of 80 plans, and a Cleaned model, obtained through the standard iterative cleaning and refinement process suggested by the vendor and in literature. The outcomes of a planning test based on 20 patients within the training library (closed loop) and 20 patients outside of the training library (open-loop) were compared through various DVH metrics and the PQM% score. RESULTS: , 80.39 ± 10.6% Cleaned and 79.4 ± 8.5% Uncleaned in the open-loop test. CONCLUSIONS: Forward feeding a RapidPlan model through a thresholding selection based on APQM% is proven to produce equal or better results than a model based on a manually and iteratively refined population. A tighter APQM% threshold turns approximately into a higher average quality of plans generated with RapidPlan. A trade-off must be found between the mean quality of the KBP library and its numerosity. The proposed KBP feeding method helps the KBP user, because it makes the model refinement more intuitive and less time consuming.

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