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Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

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A multiplan treatment-planning framework: a paradigm shift for intensity-modulated radiotherapy.

Robert R Meyer¹, Hao H Zhang, Laura Goadrich

¹Computer Sciences Department, University of Wisconsin, Madison, WI, USA.

International Journal of Radiation Oncology, Biology, Physics

|May 22, 2007

Summary

A new decision support system (DSS) aids radiotherapy planning by ranking multiple intensity-modulated radiotherapy (IMRT) plans. This system models the planning surface, improving selection of optimal treatment plans for patients.

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Area of Science:

Medical Physics
Radiation Oncology
Computational Biology

Background:

Intensity-modulated radiotherapy (IMRT) planning involves complex trade-offs between dose delivery to the target and organs at risk (OARs).
Evaluating numerous treatment plans generated by varying dose-volume constraints is challenging for radiation oncologists.
A robust system is needed to support decision-making in IMRT plan selection.

Purpose of the Study:

To introduce a framework for multiplan IMRT planning.
To describe a decision support system (DSS) for ranking multiple IMRT plans.
To model the IMRT planning surface to understand objective interdependencies.

Main Methods:

Generated 125 IMRT plans for head-and-neck and pelvic cases by altering OAR dose-volume constraints.
Utilized a DSS to rank plans based on dose-volume histogram (DVH) and equivalent uniform dose (EUD) values.
Modeled the planning surface using quadratic functions to represent plan properties.

Main Results:

The DSS offered an intuitive interface for comparing plan features, identifying 1-3 optimal plans.
Planning surface models demonstrated good predictive accuracy (<6% error) for DVH and EUD values.
Quadratic models revealed interdependencies between OAR constraint settings and plan properties.

Conclusions:

The DSS effectively assists radiation oncologists in selecting the most desirable IMRT plan.
The developed quadratic models accurately predict DVH and EUD values, showing excellent agreement with actual plan data.
This approach enhances understanding of competing planning objectives in IMRT.