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Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

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Published on: February 6, 2019

A novel energy sequence optimization algorithm for efficient spot-scanning proton arc (SPArc) treatment delivery.

Gang Liu^1,2, Xiaoqiang Li², Lewei Zhao²

¹Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China.

Acta Oncologica (Stockholm, Sweden)

|May 19, 2020

Summary

A new spot-scanning proton arc therapy (SPArc) algorithm, SPArc_seq, significantly reduces energy layer switches and beam delivery time. This optimization improves efficiency, potentially lowering proton therapy costs.

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Area of Science:

Medical Physics
Radiation Oncology
Medical Imaging

Background:

Spot-scanning proton arc therapy (SPArc) offers improved dosimetry and workflow but is hindered by energy layer switches (ELS) due to magnetic hysteresis.
Minimizing ELS is crucial for efficient SPArc plan delivery.

Purpose of the Study:

To introduce and evaluate a novel SPArc energy sequence optimization algorithm (SPArc_seq).
To assess the impact of SPArc_seq on reducing ascended ELS and beam delivery time (BDT).

Main Methods:

Implemented an iterative energy layer sorting and re-distribution mechanism within the SPArc algorithm (SPArc_orig) to create SPArc_seq.
Evaluated SPArc_seq in five disease sites (prostate, lung, brain, HNC, breast cancer).
Assessed plan quality (DVH, robustness) and BDT (fixed and dynamic arc simulations).

Main Results:

SPArc_seq achieved similar plan quality and robustness to SPArc_orig with fewer total energy layers.
SPArc_seq significantly reduced ascended ELS by 70-83% across evaluated sites.
Beam delivery time was substantially shortened by 25-65% (BDT_fixed) and 26-54% (BDT_arc).

Conclusions:

The SPArc_seq algorithm effectively reduces BDT compared to the original SPArc.
SPArc_seq's enhanced efficiency can increase patient throughput and lower proton therapy operational costs.