A Proof of Principle: Multi-Modality Radiotherapy Optimization

TL;DR

This paper introduces a mathematical framework and optimization algorithm for multi-modality radiotherapy, aiming to improve tumor damage while sparing normal tissue, validated through simple simulations aligning with clinical intuition.

Contribution

It presents a novel bilevel optimization approach for planning combined radiation modalities, addressing a complex non-convex problem in radiotherapy treatment design.

Findings

Feasibility demonstrated in a 2D phantom model.

Results align with clinical intuition.

Framework shows promise for clinical application.

Abstract

Radiotherapy is used to treat cancer patients by damaging DNA of tumor cells using ionizing radiation. Photons are the most widely used radiation type for therapy, having been put into use soon after the first discovery of X-rays in 1895. However, there are emerging interests and developments of other radiation modalities such as protons and carbon ions, owing to their unique biological and physical characteristics that distinguish these modalities from photons. Current attempts to determine an optimal radiation modality or an optimal combination of multiple modalities are empirical and in the early stage of development. In this paper, we propose a mathematical framework to optimize full radiation dose distributions and fractionation schedules of multiple radiation modalities, aiming to maximize the damage to the tumor while limiting the damage to the normal tissue to the corresponding tolerance level. This formulation gives rise to a non-convex, mixed integer program and we propose a bilevel optimization algorithm, to efficiently solve it. The upper level problem is to optimize the fractionation schedule using the dose distribution optimized in the lower level. We demonstrate the feasibility of our novel framework and algorithms in a simple 2-dimensional phantom with two different radiation modalities, where clinical intuition can be easily drawn. The results of our numerical simulations agree with the clinical intuition, validating our approach and showing the promise of the framework for further clinical investigation.