Definition of a framework for volumetric modulated arc therapy plan quality assessment with integration of dose-, complexity-, and robustness metrics.

Abstract

Background and purpose: Conventionally, the quality of radiotherapy treatment plans is assessed through visual inspection of dose distributions and dose-volume histograms. This study developed a framework to evaluate plan quality using dose, complexity, and robustness metrics. Additionally, a method for predicting plan robustness metrics using dose and complexity metrics was introduced for cases where plan robustness evaluation is unavailable or impractical.

Materials and methods: The framework and prediction models were developed and validated using 103-bronchial Volumetric Modulated Arc Therapy (VMAT)-plans. The application of the framework was demonstrated using 25-VMAT-plans. To identify significant metrics for plan evaluation, 122-metrics were analysed and narrowed down using multivariate Spearman correlation. Metric limits were set with Statistical process control (SPC). Robustness metrics were predicted using multivariable or single linear regression models based on dose-and complexity-metrics.

Results: Twenty-five-metrics were selected based on the amount and strength of correlations. R₉₅(dose coverage) and HI_95/5(homogeneity index) stood out among the dose-metrics, while the complexity-metrics showed similar correlations. Average scenarios dose at 95 % Clinical Target Volume D95_mean(CTV) and Errorbar-based Volume-Histograms (EVH) were notable for robustness metrics. Approximately 99 % of evaluated metrics fell within established SPC limits. The prediction model for D95_mean(CTV) showed good performance (adjusted R² = 0.88, mean squared error (MSE) = 3.84 × 10^-6), while the model for EVH demonstrated moderate reliability (adjusted R² = 0.52, MSE = 0.2). No statistically significant differences were found between the predicted (using dose-and complexity-metrics) and calculated robustness metrics (EVH (p-value = 0.9) and D95_mean(CTV) (p-value = 1)).

Conclusions: The developed framework enables early detection of sub-optimal, complex and non-robust treatment plans. The predictive model can be used when robustness evaluations are impractical.