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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

Formulating adaptive radiation therapy (ART) treatment planning into a closed-loop control framework.

Adam de la Zerda¹, Benjamin Armbruster, Lei Xing

¹Department of Electrical Engineering, Stanford University, Stanford, CA 94305-9505, USA.

Physics in Medicine and Biology

|August 1, 2007

Summary

Adaptive radiation therapy (ART) planning needs better algorithms. This study introduces dynamic closed-loop control algorithms that adapt to patient geometry and dose delivery, potentially improving treatment outcomes.

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Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

Area of Science:

Radiation Oncology
Medical Physics
Computational Biology

Background:

Adaptive radiation therapy (ART) holds significant promise for improving cancer treatment efficacy.
However, quantitative implementation details and effective treatment planning strategies for ART remain underexplored.
Current ART approaches often lack robust methods to integrate dose delivery history and real-time geometric changes.

Purpose of the Study:

To theoretically investigate dynamic closed-loop control algorithms for adaptive radiation therapy (ART).
To compare the utility of different algorithmic approaches using phantom and clinical data.
To assess the potential of advanced algorithms to enhance current ART practices.

Main Methods:

Development of two classes of algorithms: Adapting to Changing Geometry (ACG) and Adapting to Geometry and Delivered Dose (AGDD).
ACG algorithms account for organ deformations immediately prior to treatment.
AGDD algorithms optimize cumulative dose distribution by adapting to both pre-treatment geometric information and dose delivery history at each fraction.
Showcasing two specific AGDD algorithms for evaluation.

Main Results:

Theoretical analysis and comparison of ACG and AGDD algorithmic approaches.
Evaluation using data from both phantom studies and clinical cases.
Demonstration that certain closed-loop ART algorithms can significantly improve upon current treatment planning practices.
Identification of AGDD algorithms as particularly promising for optimizing cumulative dose.

Conclusions:

Dynamic closed-loop control algorithms represent a significant advancement for adaptive radiation therapy (ART).
Algorithms adapting to both geometry and delivered dose show potential for superior treatment planning.
Future improvements in imaging and dose verification will further enhance the value of adaptive strategies in radiation oncology.