Direct optimization of dose-volume histogram metrics in radiation therapy treatment planning

TL;DR

This paper introduces a novel mathematical framework for directly optimizing dose-volume histogram metrics in radiation therapy, leading to improved plan quality and reduced user interaction.

Contribution

It develops a differentiable functional approach to optimize DVH metrics directly, connecting to risk measures and enabling better control of dose tails.

Findings

Marginally better clinical goal fulfillment

Improved control of dose tails in target distributions

Potential to reduce user interaction in planning

Abstract

We present a method of directly optimizing on deviations in clinical goal values in radiation therapy treatment planning. Using a new mathematical framework in which metrics derived from the dose-volume histogram are regarded as functionals of an auxiliary random variable, we are able to obtain volume-at-dose and dose-at-volume as infinitely differentiable functions of the dose distribution with easily evaluable function values and gradients. Motivated by the connection to risk measures in finance, which is formalized in this framework, we also derive closed-form formulas for mean-tail-dose and demonstrate its capability of reducing extreme dose values in tail distributions. Numerical experiments performed on a prostate and a head-and-neck patient case show that the direct optimization of dose-volume histogram metrics produced marginally better results than or outperformed conventional planning objectives in terms of clinical goal fulfilment, control of low- and high-dose tails of target distributions and general plan quality defined by a pre-specified evaluation measure. The proposed framework eliminates the disconnect between optimization functions and evaluation metrics and may thus reduce the need for repetitive user interaction associated with conventional treatment planning. The method also has the potential of enhancing plan optimization in other settings such as multicriteria optimization and automated treatment planning.