A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning

TL;DR

This paper introduces a novel K-adaptability heuristic for robust proton therapy planning, reducing conservativeness and improving worst-case dose coverage compared to traditional methods.

Contribution

It develops an efficient heuristic for K-adaptability in proton therapy, enhancing robustness and computational efficiency over existing approaches.

Findings

Increased worst-case dose coverage by up to 4.52 Gy on average.

Demonstrated superiority in objective value and time-efficiency.

Effective scenario clustering improves plan robustness.

Abstract

Uncertainties such as setup and range errors can significantly compromise proton therapy. A discrete uncertainty set is often constructed to represent different uncertainty scenarios. A min-max robust optimization approach is then utilized to optimize the worst-case performance of a radiation therapy plan against the uncertainty set. However, the min-max approach can be too conservative as a single plan has to account for the entire uncertainty set. K-adaptability is a novel approach to robust optimization which covers the uncertainty set with multiple (K) solutions, reducing the conservativeness. Solving K-adaptability to optimality is known to be computationally intractable. To that end, we developed a novel and efficient K-adaptability heuristic that iteratively clusters the scenarios based on plan-scenario performance for the proton radiation therapy planning problem. Compared to the conventional robust solution, the developed K-adaptability heuristic increased the worst-case CTV Dmin dose up to 4.52 Gy on average across five head and neck patients. The developed heuristic also demonstrated its superiority in objective value and time-efficiency compared to the competing methods we tested.