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Mixed integer programming improves comprehensibility and plan quality in inverse optimization of prostate HDR

Bram L Gorissen¹, Dick den Hertog, Aswin L Hoffmann

¹Department of Econometrics and Operations Research/Center for Economic Research (CentER), Tilburg University, PO Box 90153, Tilburg 5000-LE, the Netherlands. b.l.gorissen@tilburguniversity.edu

Physics in Medicine and Biology

|February 1, 2013

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New methods for prostate HDR brachytherapy planning significantly reduce solution times for optimizing catheter positions and dwell times. A novel dose-volume-based model improves plan quality and DVH statistics compared to existing dose-based approaches.

Area of Science:

Medical Physics
Radiation Oncology
Computational Biology

Background:

Current inverse treatment planning for prostate HDR brachytherapy relies on objective functions lacking direct DVH interpretation.
Existing linear and quadratic programming models often yield suboptimal plans or have lengthy solution times.

Purpose of the Study:

To develop faster and more clinically relevant inverse treatment planning methods for prostate HDR brachytherapy.
To improve plan quality and DVH outcomes by incorporating direct DVH criteria into the optimization process.

Main Methods:

Implemented mixed integer programming to accelerate optimization of catheter positions and dwell times.
Developed an iterative procedure for quadratic programming to allow interval-based dose prescription.

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Formulated a new dose-volume-based model optimizing V(100%) and satisfying DVH criteria.

Main Results:

Reduced solution times for LP and QP models, with early solver termination yielding a 75% speed-up.
The iterative QP procedure matched LP model speed and produced superior plans compared to non-iterative QP.
The new dose-volume-based model optimized plans within minutes, offering better DVH statistics than dose-based models.

Conclusions:

Optimizing catheter positions and dwell times using dose-volume criteria offers superior clinical plan quality in prostate HDR brachytherapy.
Existing dose-based models show a weak correlation between objective value and clinical plan quality.
Faster, DVH-driven optimization is achievable, enhancing treatment planning efficiency and outcomes.