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Updated: Dec 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

A plan template-based automation solution using a commercial treatment planning system.

Xiaotian Huang^1,2, Hong Quan¹, Bo Zhao³

¹School of Physics and Technology, Wuhan University, Wuhan, China.

Journal of Applied Clinical Medical Physics

|March 18, 2020

Summary

This summary is machine-generated.

This study developed an automated radiotherapy planning platform that interfaces with Monaco treatment planning system (TPS). The platform generates high-quality, robust treatment plans for prostate and head and neck cancers with minimal user intervention.

Keywords:

VMAT auto-planning inverse planning template

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Published on: February 6, 2019

Area of Science:

Medical Physics
Radiation Oncology
Computational Biology

Background:

Automated treatment planning systems (TPS) aim to improve efficiency and consistency in radiation therapy.
Developing robust auto-planning platforms requires integration with commercial TPS and sophisticated optimization algorithms.

Purpose of the Study:

To develop an auto-planning platform interfaced with the Monaco TPS for creating robust, high-quality radiotherapy plans.
To achieve dosimetric requirements for diverse anatomical sites and patient cases.

Main Methods:

A rule-based system external to Monaco was developed to modify plan templates iteratively.
Robot Framework was used to automate planning trials, adjusting optimization constraints based on dosimetric evaluations.
The platform was validated on 10 prostate and 10 head-and-neck cancer cases.

Main Results:

Auto-generated plans for prostate cancer showed comparable quality to clinical plans, with reduced bladder dose.
Head and neck auto-plans demonstrated improved conformity and reduced normal tissue dose, particularly to the spinal cord and brain stem.
Acceptable plans were achieved within 30 optimization trials, with weak dependence on initial template parameters.

Conclusions:

An automated planning platform integrated with the Monaco TPS was successfully developed.
Preliminary validation on prostate and head and neck cases indicates the platform's potential for efficient and effective radiotherapy planning.