Predicting deliverability of volumetric‐modulated arc therapy (VMAT) plans using aperture complexity analysis

Abstract

The purpose of this study was to evaluate the ability of an aperture complexity metric for volumetric-modulated arc therapy (VMAT) plans to predict plan delivery accuracy. We developed a complexity analysis tool as a plug-in script to Varian’s Eclipse treatment planning system. This script reports the modulation of plans, arcs, and individual control points for VMAT plans using a previously developed complexity metric. The calculated complexities are compared to that of 649 VMAT plans previously treated at our institution from 2013 to mid-2015. We used the VMAT quality assurance (QA) results from the 649 treated plans, plus 62 plans that failed pretreatment QA, to validate the ability of the complexity metric to predict plan deliverability. We used a receiver operating characteristic (ROC) analysis to determine an appropriate complexity threshold value above which a plan should be considered for reoptimization before it moves further through our planning workflow. The average complexity metric for the 649 treated plans analyzed with the script was 0.132 mm−1 with a standard deviation of 0.036 mm−1. We found that when using a threshold complexity value of 0.180 mm−1, the true positive rate for correctly identifying plans that failed QA was 44%, and the false-positive rate was 7%. Used clinically with this threshold, the script can identify overly modulated plans and thus prevent a significant portion of QA failures. Reducing VMAT plan complexity has a number of important clinical benefits, including improving plan deliverability and reducing treatment time. Use of the complexity metric during both the planning and QA processes can reduce the number of QA failures and improve the quality of VMAT plans used for treatment.

Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning

“…Most of these features are related to beam complexity. This finding agrees with our understanding of the strong correlation between the gamma passing rate and the level of complexity of a treatment plan (see also Section 2.B). It is important to note that MAD (the maximum distance from the middle point of each leaf pair to the imager center) and MAXJ (maximum jaw position) are among the top 10 features.…”

“…The QA prediction enables identification of failing plans early in the process so that they can be re‐planned immediately rather than waiting until the measurement results are available for analysis at a later time, enabling physicists to better identify plans prone to QA failures and to develop proactive QA approaches. Among the many aforementioned sources of error and uncertainty, it is generally agreed that IMRT delivery accuracy, and thus the gamma passing rates, are heavily contingent on plan complexity with increased complexity resulting in a decreased passing rate . A highly intensity modulated computer‐generated plan usually provides superior dose distributions but may lead to a poor gamma passing rate.…”

“…A highly intensity modulated computer‐generated plan usually provides superior dose distributions but may lead to a poor gamma passing rate. Several publications have demonstrated promising results in correlating IMRT QA passing rates with beam complexity scores . Machine learning and deep learning have recently been used to predict IMRT QA passing rates .…”

“…Among the many aforementioned sources of error and uncertainty, it is generally agreed that IMRT delivery accuracy, and thus the gamma passing rates, are heavily contingent on plan complexity with increased complexity resulting in a decreased passing rate. [7][8][9][10][11] A highly intensity modulated computer-generated plan usually provides superior dose distributions but may lead to a poor gamma passing rate. Several publications have demonstrated promising results in correlating IMRT QA passing rates with beam complexity scores.…”

“…Several publications have demonstrated promising results in correlating IMRT QA passing rates with beam complexity scores. 7,[9][10][11] Machine learning and deep learning have recently been used to predict IMRT QA passing rates. [12][13][14][15][16][17][18] Among these reports Valdes et al have developed a machine learning (ML) method (Poisson regression with Lasso regularization) that is able to predict IMRT QA results with an error <3%.…”