Increased accuracy of planning tools for optimization of dynamic multileaf collimator delivery of radiotherapy through reformulated objective functions

TL;DR

This study introduces a novel formulation of objective functions for radiotherapy planning that incorporates DMLC delivery constraints, leading to improved plan quality and potentially streamlining the planning process.

Contribution

The paper presents a new objective function formulation with DMLC constraints and an efficient interior point method, enhancing plan accuracy and reducing trial-and-error in radiotherapy planning.

Findings

DMLC plans with the new objective functions are Pareto optimal.

Proposed plans show comparable or superior quality to conventional plans.

The method simplifies the planning process by reducing parameter tuning.

Abstract

The purpose of this study is to examine in a clinical setting a novel formulation of objective functions for intensity-modulated radiotherapy treatment plan multicriteria optimization (MCO) that we suggested in a recent study. The proposed objective functions are extended with dynamic multileaf collimator (DMLC) delivery constraints from the literature, and a tailored interior point method is described to efficiently solve the resulting optimization formulation. In a numerical planning study involving three patient cases, DMLC plans Pareto optimal to the MCO formulation with the proposed objective functions are generated. Evaluated based on pre-defined plan quality indices, these DMLC plans are compared to conventionally generated DMLC plans. Comparable or superior plan quality is observed. Supported by these results, the proposed objective functions are argued to have a potential to streamline the planning process, since they are designed to overcome the methodological shortcomings associated with the conventional penalty-based objective functions assumed to cause the current need for time-consuming trial-and-error parameter tuning. In particular, the increased accuracy of the planning tools imposed by the proposed objective functions has the potential to make the planning process less complicated. These conclusions position the proposed formulation as an alternative to existing methods for automated planning.