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SUMMARY:Validating a mathematical model of brain tumour growth with the ap
parent diffusion coefficient
DTSTART;VALUE=DATE-TIME:20180709T094500Z
DTEND;VALUE=DATE-TIME:20180709T100000Z
DTSTAMP;VALUE=DATE-TIME:20241102T205238Z
UID:indico-contribution-405@conferences.maths.unsw.edu.au
DESCRIPTION:Speakers: Pamela R. Jackson (Precision Neurotherapeutics Innov
ation Program\, Department of Neurosurgery\, Mayo Clinic)\nThe Proliferati
on-Invasion (PI) mathematical model of patient specific glioblastoma (GBM)
growth utilizes T1 and T2-weighted/fluid attenuated inversion recovery (F
LAIR) magnetic resonance (MR) images to estimate net proliferation (`ρ`)
and net invasion (D) rates. We have previously developed methods to parame
trize this model from these routine MRIs such that higher D/`ρ` tumours a
re considered more invasive than lower D/`ρ` tumours. More advanced MR im
aging methods such as diffusion-weighted imaging (DWI) allow for the calcu
lation of quantitative measurements. The apparent diffusion coefficient (A
DC)\, which is calculated from DWI\, is believed to be predictive of tumou
r cellularity and microstructure. Our objective is to validate that the PI
model’s measure of invasiveness D/`ρ` derived from routine MRIs is con
sistent with quantitative MRI measurements. We hypothesize that D/`ρ` wil
l be correlated with the distributions of ADC values within the tumour. Fu
rther\, we expect that the proportion of high ADC values values (low cellu
larity) will predominate for low D/`ρ` (highly invasive) tumours. T1Gd a
nd FLAIR images were segmented and regions of interest (ROIs) created for
patients with contrast-enhancing GBMs. A FLAIR penumbra ROI was created by
excluding the T1Gd ROI from the corresponding FLAIR ROI. ROI’s were the
n used to mask the co-registered ADC maps and the ADC values from within t
he ROI were plotted as a histogram. The ADC histogram from the FLAIR ROI w
as fit using a bimodal Gaussian model. ADC histogram peak boundaries were
calculated as being +/- one averaged standard deviation from the averaged
peak location (FLAIR ROI histograms). The averaged boundaries were then ap
plied to each histogram. We calculated the percent of ADC voxels classifie
d as being below each peak\, within the lower or upper peak\, within both
peaks\, or above the peaks. The percentage of voxels within each region we
re then plotted against D/`ρ`. Understanding the relationship between D/`
ρ` and ADC allows us to connect observations on multiparametric imaging a
nd elucidate the tumour biology. These results support the practical appli
cability of the PI mathematical model in quantifying patient-specific inva
sion characteristics by cross-correlating those findings with that seen on
other imaging techniques and parameters\, in this case ADC.\n\nhttps://co
nferences.maths.unsw.edu.au/event/2/contributions/405/
LOCATION:University of Sydney Holme Building/--The Refectory
URL:https://conferences.maths.unsw.edu.au/event/2/contributions/405/
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