Abstract
Quantitative magnetization transfer (qMT) was used as a biomarker to monitor glioblastoma (GBM) response to chemo-radiation and identify the earliest time-point qMT could differentiate progressors from non-progressors. Nineteen GBM patients were recruited and MRI-scanned before (Day0), two weeks (Day14), and four weeks (Day28) into the treatment, and one month after the end of the treatment (Day70). Comprehensive qMT data was acquired, and a two-pool MT model was fit to the data. Response was determined at 3–8 months following the end of chemo-radiation. The amount of magnetization transfer ({bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}}}) was significantly lower in GBM compared to normal appearing white matter (p < 0.001). Statistically significant difference was observed in {bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}}} at Day0 between non-progressors (1.06 ± 0.24) and progressors (1.64 ± 0.48), with p = 0.006. Changes in several qMT parameters between Day14 and Day0 were able to differentiate the two cohorts with {bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}}} providing the best separation (relative {bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}},{bf{Non}}-{bf{progressor}}} = 1.34 ± 0.21, relative {bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}},{bf{progressor}}} = 1.07 ± 0.08, p = 0.031). Thus, qMT characteristics of GBM are more sensitive to treatment effects compared to clinically used metrics. qMT could assess tumor aggressiveness and identify early progressors even before the treatment. Changes in qMT parameters within the first 14 days of the treatment were capable of separating early progressors from non-progressors, making qMT a promising biomarker to guide adaptive radiotherapy for GBM.
Highlights
The current study investigates the changes in the Quantitative magnetization transfer (qMT) parameters in GBM over the course of its 6-weeks of chemo-radiation treatment and attempts to find the earliest time point at which qMT could separate early progressors from non-progressors
The two-pool Magnetization transfer (MT) model was fit to MRI data voxel-by-voxel for each patient at each time point and the four model parameters were calculated
The MT model parameters for contralateral normal appearing white matter (cNAWM) at each time point were reported in Table 1, demonstrating there were no statistically significant differences between the values at different time points
Summary
Three out of the total 19 recruited patients were removed from analysis due to significant imaging artifacts (one case), patient requested termination of the baseline scan (one case), R1/B1 mapping data was missing (one case). The histogram distribution of the two main qMT parameter representing both pools in the qMT model, i.e. amount of magnetization transfer (RM0b/Ra) and the direct effect of the free water pool (1/(RaT2a)) for each patient at baseline (Day0) and Day[14] scans (for ROI type I) are shown in the supplementary material Figs S2 and S3. We show that the progressor and non-progressor have different spatial distributions of these parameters.
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