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

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

Published on: February 6, 2019

A novel energy layer optimization framework for spot-scanning proton arc therapy.

Wenbo Gu¹, Dan Ruan¹, Qihui Lyu¹

¹Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.

Medical Physics

|February 11, 2020

Summary

A new method for spot-scanning proton arc therapy (SPAT) optimizes energy layers and sequencing, significantly reducing planning time and improving treatment efficiency. This integrated approach enhances dosimetry and delivery speed for proton therapy.

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Area of Science:

Medical Physics
Radiation Oncology
Computational Optimization

Background:

Spot-scanning proton arc therapy (SPAT) is an advanced technique for improving radiation plan conformity and delivery efficiency.
Existing SPAT algorithms use heuristic methods for energy layer selection and sequencing, which do not guarantee optimal dosimetry or efficiency.
There is a need for an integrated framework to optimize both energy layer switching and dosimetry in SPAT.

Purpose of the Study:

To develop an integrated method for optimizing energy layer selection and sequencing in SPAT.
To address the limitations of existing greedy and heuristic approaches in SPAT planning.
To improve both the dosimetric quality and delivery efficiency of SPAT.

Main Methods:

Formulated Energy Layer Optimization for Spot-Scanning Proton Arc Therapy (ELO-SPAT) using dose fidelity, group sparsity, log barrier regularization, and an energy sequencing (ES) penalty.
Implemented group sparsity and log barrier functions to select one energy layer per control point.
Incorporated an ES penalty to sort delivery from high to low energy, minimizing total energy layer switching time (ELST) and penalizing time-consuming energy switch-ups.

Main Results:

ELO-SPAT reduced optimization runtime by 84% compared to the greedy SPArc method.
ES regularization reduced energy switch-ups from 40-60 to under 20, decreasing ELST by 24% (synchrotron) and 14% (cyclotron) compared to energy-sequenced SPArc.
ELO-SPAT and SPArc plans showed improved Organ-at-Risk (OAR) sparing over Intensity-Modulated Proton Therapy (IMPT); ELO-SPAT without ES further improved OAR sparing (avg. [1.57, 3.34] GyRBE reduction in Dmean, Dmax).

Conclusions:

Developed a computationally efficient integrated optimization method for SPAT.
ELO-SPAT effectively solves energy layer selection and sequencing problems.
The method generates SPAT plans with good dosimetry and high delivery efficiency.