Zachary R. L. Boyd1, William A. Hall2, Douglas E. Prah2, and Eric S. Paulson1,2,3
1Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States, 2Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States, 3Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
Synopsis
Tumor
burden, tumor proliferation, and tumor hypoxia, all of which vary in space and
time, are evidence-based contributors of radiotherapy failure. In addition, it has been demonstrated that
gene expression can influence radiosensitivity.
We demonstrate here the initial feasibility of a framework to
incorporate genomic and radiographic information to derive patient-specific,
voxelwise radiation dose prescription maps for use in a biologically adaptive
MR-guided radiotherapy (BAMRgRT) strategy.
Introduction
The
objective of conventional radiotherapy is to deliver uniform doses to tumor
targets while minimizing dose to surrounding organs at risk. However, tumor burden, tumor proliferation,
and tumor hypoxia, evidence-based contributors of radiotherapy failure, can vary
over space and time. Furthermore, it has
recently been demonstrated that gene expression can influence radiosensitivity1. We
demonstrate here a framework to incorporate genomic and radiographic information
to derive patient-specific, voxelwise radiation dose prescription maps for use in a
biologically adaptive MR-guided radiotherapy (BAMRgRT) strategy.Methods
The
feasibility of genomically and radiographically adjusted dose (GRAD) for
BAMRgRT was tested in two brain tumor patients.
A quantitative MRI (qMRI) panel consisting of i) multiple flip angle T1
mapping (3, 6, 10, 20, 30 degrees), ii) accelerated CPMG T2 mapping (11-200
msec), iii) IVIM DWI (10 b-values), and iv) pharmacokinetic DCE-MRI was acquired in each
patient. To assess the absolute bias and
short-term repeatability of qMRI parameters, calibration experiments were
performed using the Eurospin TO5 and QIBA diffusion phantoms on an Elekta 1.5T
MR-Linac and Siemens 1.5T and 3.0T MR simulators. A reference genomic-adjusted radiation dose (GARD)
score was calculated for a conventional 2 Gy per fraction treatment using a
population radiosensitivity index (RSI) of 0.2 and beta of 0.0671. The GARD
score was then used to determine the required dose for a patient-specific RSI
of 0.25. For each patient, gross tumor
and high-risk tumor volumes were manually segmented on the qMRI parameter maps. A voxelwise 3D dose prescription map was then
calculated in MIM Maestro utilizing the linear-quadratic model extended to
incorporate GARD. An inverted copy of
the calculated 3D dose prescription map was also constructed for use in
treatment planning. Synthetic CT images,
generated from a high resolution 3D T1 FLASH image of each patient using a
conditional GAN2, and inverted 3D dose prescription maps were loaded
into the Elekta Monaco radiation treatment planning system (RTPS). Volumetric modulated arc therapy (VMAT) plans
(3mm dose grid, 1% statistical uncertainty per calculation) were calculated with
dose painting by numbers (DPBN)3, facilitated by using the inverted dose prescription
map as a bias dose during plan optimization.
Finally, sample VMAT plans were calculated in phantom to determine the
modulation transfer function (MTF) of Monaco, in order to investigate the
degree to which the RTPS is able to modulate dose of proximal clusters of high-risk
tumor cells. Results
Figure
1 displays a qMRI panel for one representative patient. Figure 2 displays results of calibration
experiments for T1, T2, and apparent diffusion coefficient parameter maps. The accuracy and short-term repeatability of
the MR-Linac was comparable to the MR simulators studied. Figure 3 displays sample forward and inverted
3D dose prescription maps overlaid onto a synthetic CT calculated for one of
the patients studied. The tumor regions
were manually segmented on the qMR images.
The two dose levels of 58.6 Gy and 75.3 Gy were obtained using the
linear-quadratic model extended to incorporate GARD. Figure 4 displays a DPBN radiation treatment
plan calculated in Monaco using the inverted dose prescription map as a bias
dose. Both PTVs were able to achieve
over 95% coverage and the global max dose of the plan was approximately 16%. Figure 5 displays the MTF results of the
Monaco RTPS. For 1 cm diameter targets, about
a 40% modulation of dose was achievable with a 3.5 cm lattice spacing. The dose modulation reached an asymptote of
about 50% beyond a lattice spacing of about 5 cm.Discussion
These
initial results demonstrate the feasibility of a framework to genomically and
radiographically adjust radiotherapy doses for BAMRgRT. The inverted dose prescription maps
facilitate DPBN on commercial RTPS, which normally do not support voxelwise objective
functions. Once a treatment plan is
obtained, the forward dose prescription map can be employed to evaluate the
calculated plan quality or expected tumor control probability. As shown by the MTF study, the Monaco RTPS has
limitations in the extent to which dose can be modulated. This may have implications on DPBN of small,
high-risk tumor cell clusters demanding rapid variations in dose. Future work will include development of
machine learning methods to automatically segment gross and high-risk tumor
regions from qMRI parameter maps.Conclusion
A feasible
framework has been created to incorporate genomic and radiographic information
to derive patient-specific radiation dose prescription maps for use in a
biologically adaptive MR-guided radiotherapy (BAMRgRT) strategy.Acknowledgements
Advancing a Healthier Wisconsin (5520223/5520231)References
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