Abstract
Knowledge‐based planning (KBP) can be used to estimate dose–volume histograms (DVHs) of organs at risk (OAR) using models. The task of model creation, however, can result in estimates with differing accuracy; particularly when outlier plans are not properly addressed. This work used RapidPlan™ to create models for the prostate and head and neck intended for large‐scale distribution. Potential outlier plans were identified by means of regression analysis scatter plots, Cook's distance, coefficient of determination, and the chi‐squared test. Outlier plans were identified as falling into three categories: geometric, dosimetric, and over‐fitting outliers. The models were validated by comparing DVHs estimated by the model with those from a separate and independent set of clinical plans. The estimated DVHs were also used as optimization objectives during inverse planning. The analysis tools lead us to identify as many as 7 geometric, 8 dosimetric, and 20 over‐fitting outliers in the raw models. Geometric and over‐fitting outliers were removed while the dosimetric outliers were replaced after re‐planning. Model validation was done by comparing the DVHs at 50%, 85%, and 99% of the maximum dose for each OAR (denoted as V50, V85, and V99) and agreed within −2% to 4% for the three metrics for the final prostate model. In terms of the head and neck model, the estimated DVHs agreed from −2.0% to 5.1% at V50, 0.1% to 7.1% at V85, and 0.1% to 7.6% at V99. The process used to create these models improved the accuracy for the pharyngeal constrictor DVH estimation where one plan was originally over‐estimated by more than twice. In conclusion, our results demonstrate that KBP models should be carefully created since their accuracy could be negatively affected by outlier plans. Outlier plans can be addressed by removing them from the model and re‐planning.
Highlights
Knowledge-based planning (KBP) is an emerging field in radiation therapy which uses machine learning techniques to estimate radiation therapy dose
Outlier plans were identified as falling into three categories: geometric, dosimetric, and over-fitting outliers
Our results demonstrate that KBP models should be carefully created since their accuracy could be negatively affected by outlier plans
Summary
Knowledge-based planning (KBP) is an emerging field in radiation therapy which uses machine learning techniques to estimate radiation therapy dose. KBP can be generalized to be the automation of different steps in the creation of a plan based on past practice. These steps can range from the estimation of field direction, weights of optimization objectives, and even dose distribution.. Radiation treatment planning is a complex process which can result in an infinite number of plans; some of which are suboptimal. This is because the final dose distribution is dependent on the geometry of the organs at risk (OAR) with respect to the target. Other factors that can potentially affect the quality of the final plan are differences in dose prescription, treatment technique, and planner experience. It is because of these reasons that plan quality evaluation has been based on user experience primarily making the development of quantitative tools necessary
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