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At various stage of progression, most brain tumors are not homogenous. In this presentation, we retrospectively studied
the distribution of ADC values inside tumor volume during the course of tumor treatment and progression for a selective
group of patients who underwent an anti-VEGF trial. Complete MRI studies were obtained for this selected group of
patients including pre- and multiple follow-up, post-treatment imaging studies. In each MRI imaging study, multiple
scan series were obtained as a standard protocol which includes T1, T2, T1-post contrast, FLAIR and DTI derived
images (ADC, FA etc.) for each visit. All scan series (T1, T2, FLAIR, post-contrast T1) were registered to the
corresponding DTI scan at patient's first visit. Conventionally, hyper-intensity regions on T1-post contrast images are
believed to represent the core tumor region while regions highlighted by FLAIR may overestimate tumor size. Thus we
annotated tumor regions on the T1-post contrast scans and ADC intensity values for pixels were extracted inside tumor
regions as defined on T1-post scans. We fit a mixture Gaussian (MG) model for the extracted pixels using the
Expectation-Maximization (EM) algorithm, which produced a set of parameters (mean, various and mixture coefficients)
for the MG model. This procedure was performed for each visits resulting in a series of GM parameters. We studied the
parameters fitted for ADC and see if they can be used as indicators for tumor progression. Additionally, we studied the
ADC characteristics in the peri-tumoral region as identified by hyper-intensity on FLAIR scans. The results show that
ADC histogram analysis of the tumor region supports the two compartment model that suggests the low ADC value subregion
corresponding to densely packed cancer cell while the higher ADC value region corresponding to a mixture of
viable and necrotic cells with superimposed edema. Careful studies of the composition and relative volume of the two
compartments in tumor region may provide some insights in the early assessment of tumor response to therapy for
recurrence brain cancer patients.
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