Automated VMAT treatment planning using sequential convex programming: algorithm development and clinical implementation

Abstract

Objective: To develop and clinically implement a fully automated treatment planning system for volumetric modulated arc therapy (VMAT).
Approach: We solve two constrained optimization problems sequentially. The tumor coverage is maximized at the first step while respecting all maximum/mean dose clinical criteria. The second step further reduces the dose at the surrounding organs-at-risk (OARs) as much as possible. Our algorithm optimizes the machine parameters (leaf positions and monitor units) directly and the resulting mathematical non-convexity is handled using the sequential convex programming by solving a series of convex approximation problems. We directly integrate two novel convex surrogate metrics to improve plan delivery efficiency and reduce plan complexity by promoting aperture shape regularity and neighboring aperture similarity. The entire workflow is automated using the Eclipse treatment planning system (TPS) application program interface (API) scripting and provided to users as a plug-in, requiring the users to solely provide the contours and their preferred arcs. Our program provides the optimal machine parameters and does not utilize the Eclipse optimization engine, however, it utilizes the Eclipse final dose calculation engine. We have tested our program on 60 patients of different disease sites and prescriptions for stereotactic body radiotherapy (SBRT) (paraspinal (24Gy x 1, 9Gy x 3), oligometastis (9Gy x 3), lung (18Gy x 3, 12Gy x 4)) and retrospectively compared the automated plans with the manual plans used for treatment. The program is currently deployed in our clinic and being used in our daily clinical routine to treat patients. 
Main results: The automated plans found dosimetrically comparable or superior to the manual plans. For paraspinal (24Gy x 1), the automated plans especially improved tumor coverage (the average PTV95% from 96% to 98% and CTV100% from 95% to 97%) and homogeneity (the average PTV maximum dose from 120% to 116%). For other sites/prescriptions, the automated plans especially improved the duty cycle (23%-39.4%).
Significance: This work proposes a fully automated approach to the mathematically challenging VMAT problem. It also shows how the capabilities of the existing FDA-approved commercial TPS can be enhanced using an in-house developed optimization algorithm that completely replaces the TPS optimization engine. The code and pertained models along with a sample dataset will be released on our ECHO-VMAT GitHub (https://github.com/PortPy-Project/ECHO-VMAT).