Abstract
Many quantitative metrics have been proposed in literature for characterization of spatial dose properties. The aim of this study is to work towards much-needed consensus in the radiotherapy community on which of these metrics to use. We do this by comparing characteristics of the metrics and providing a systematically selected set of metrics to comprehensively quantify properties of the spatial dose distribution. We searched the literature for metrics to quantitatively evaluate dose conformity, homogeneity, gradient (overall and directional), and distribution and location of over- and under-dosed sub-volumes. For each spatial dose property, we compared the responses of its corresponding metrics to simulated dose variations in a virtual water phantom. Selection criteria were a metric's ability to describe simulated scenarios robustly and to be visualized in an intuitive way. We saw substantial differences in the responses of metrics to the simulated dose variations. Some conformity and homogeneity metrics were unable to quantify certain types of changes (e.g. target under-coverage). Others showed a large dependency on the shape and volume of targets and isodoses. Metric values differed between calculations in a static plan and in simulated full treatment courses including setup errors, especially for metrics quantifying distribution and location of hot and cold spots. We provide an Eclipse plugin script to calculate and visualize selected metrics. The selected set of metrics provides complementary and comprehensive quantitative information about the spatial dose distribution. This work serves as a step towards broader consensus on the use of spatial dose metrics.
Highlights
The need for consistent treatment plan comparison is ubiquitous in clinical radiotherapy practice
The visualization methods we found in literature were the Spa tialDVH as proposed by Zhao et al [28], the dose-location histogram (DLH) as implemented in the Computational Environment for Radiology Research (CERR) [30], and the vectorized dose-volume histogram (DVH) proposed by Mayo et al [31]
We have implemented these metrics in a ready-to-use toolbox to aid in treatment planning
Summary
The need for consistent treatment plan comparison is ubiquitous in clinical radiotherapy practice. Radiotherapy physicians and physicists often find themselves in a situation where they must select the optimal treatment plan for a patient among a group of competing plans. These plans might differ in modality (e.g. conformal vs modulated treat ments), planning technique (e.g. automated vs manual), or be sequential iterations in the planning process. The status quo in treatment plan comparison is mainly built upon dose-volume histogram (DVH) metrics, visual inspection of dose distributions, and (to some extent) modeling of tumor control and normal tissue complication probabilities (TCP/NTCP). An important shortcoming of the DVH, is that information about the spatial distribution of dose within a target or organ-at-risk (OAR) is lost
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