  Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation
beamlets and is a proposed treatment approach for several tumour diagnoses that
currently have poor clinical treatment outcomes, such as gliosarcomas.
Prescription dose estimations for treating preclinical gliosarcoma models in
MRT studies at the Imaging and Medical Beamline at the Australian Synchrotron
currently rely on Monte Carlo (MC) simulations. The steep dose gradients
associated with the 50$\,\mu$m wide coplanar beamlets present a significant
challenge for precise MC simulation of the MRT irradiation treatment field in a
short time frame. Much research has been conducted on fast dose estimation
methods for clinically available treatments. However, such methods, including
GPU Monte Carlo implementations and machine learning (ML) models, are
unavailable for novel and emerging cancer radiation treatment options like MRT.
In this work, the successful application of a fast and accurate machine
learning dose prediction model in a retrospective preclinical MRT rodent study
is presented for the first time. The ML model predicts the peak doses in the
path of the microbeams and the valley doses between them, delivered to the
gliosarcoma in rodent patients. The predictions of the ML model show excellent
agreement with low-noise MC simulations, especially within the investigated
tumour volume. This agreement is despite the ML model being deliberately
trained with MC-calculated samples exhibiting significantly higher statistical
uncertainties. The successful use of high-noise training set data samples,
which are much faster to generate, encourages and accelerates the transfer of
the ML model to different treatment modalities for other future applications in
novel radiation cancer therapies.
