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Updated: Jul 26, 2025

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

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

Pınar Dursun¹, Linda Hong¹, Gourav Jhanwar¹

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.

Physics in Medicine and Biology

|June 21, 2023

Summary

This summary is machine-generated.

A new automated treatment planning system (TPS) for volumetric modulated arc therapy (VMAT) offers comparable or superior results to manual planning. This VMAT TPS improves tumor coverage and plan homogeneity, enhancing patient treatment.

Keywords:

VMAT optimization automated planning direct machine parameter optimization sequential convex programming

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Area of Science:

Medical Physics
Radiation Oncology
Computational Biology

Background:

Volumetric modulated arc therapy (VMAT) planning is complex and time-consuming.
Existing treatment planning systems (TPS) have limitations in optimizing VMAT plans.
Automated solutions are needed to improve efficiency and plan quality in VMAT.

Purpose of the Study:

To develop and clinically implement a fully automated TPS for VMAT.
To optimize machine parameters directly for improved VMAT plan delivery.
To enhance VMAT planning by promoting aperture shape regularity and similarity.

Main Methods:

Sequential convex programming to solve constrained optimization problems.
Direct optimization of machine parameters (leaf positions, monitor units).
Integration of novel convex surrogate metrics for plan efficiency and complexity reduction.
Automation via Eclipse TPS scripting as a plug-in module.

Main Results:

Automated VMAT plans were dosimetrically comparable or superior to manual plans.
Improved tumor coverage (e.g., PTV 95% from 96% to 98%) and homogeneity for paraspinal treatments.
Enhanced duty cycle (23%-39.4%) for other disease sites and prescriptions.
Successful clinical deployment and daily use in routine patient treatment.

Conclusions:

A fully automated VMAT planning approach is feasible and effective.
In-house optimization algorithms can enhance commercial TPS capabilities.
The developed system offers improved plan quality and efficiency for VMAT treatments.