The aim of this study was to investigate whether functional MR parameters obtained pre-surgery and post-chemoradiotherapy, could be used to predict overall survival in a cohort of patients with glioblastoma multiforme. Furthermore, the study aimed to investigate if certain parameters provided greater prognostic information at specific time points. This work may be of interest to both clinicians and clinical scientists.

Multiparametric MR data was acquired from 30 patients with histologically proven glioblastoma multiforme prior to surgery (TP1) and 2 weeks post-chemoradiotherapy (TP2) (mean scan interval = 99±11 days). All data was acquired using a 3.0T GE 750 Discovery system and eight channel phased array head coil. Morphological imaging was acquired with the addition of DTI (32 directions), T₁ DCE (tdel=5sec) with 5 pre-contrast flip angles, and T₂* DSC (tdel=2sec).

Motion correction and registration was applied using FSL^{1, 2}. Data was processed using in-house developed software. Calculated DTI parameters were: mean diffusivity (MD), fractional anisotropy (FA), anisotropic component of diffusion (q), longitudinal (LD), and radial diffusivity (RD). Pharmacokinetic modelling using a two compartment Tofts-Kety model was applied to the DCE-MRI data transformed to contrast concentration using T₁ values calculated from multi-flip angle data (R1). DSC-MRI was processed using a contrast agent extravasation correction model³ and normalised to global white matter (rCBV). Parametric volumes were created by registering all maps into a single 4D [x, y, z, parameter] volume².

Volumes of interest (TUM) were manually contoured using morphological imaging (T₂ abnormality + T₁ post-contrast abnormality – necrosis/cyst – haemorrhage). Mean values were calculated for each parameter. Gaussian mixture modelling (limited to 2 populations) was applied to the VOI of each parameter map (Figure 1), generating a further two means, sorted in ascending order, and labelled P0 and P1 respectively. Parameters were dichotomised using the median value of each measure prior to Kaplan-Meier survival analysis with Log rank tests used to calculate significant survival differences between groups. Cox regression survival analysis was subsequently implemented using a forward Wald methodology to evaluate interactions between time and MR parameters.

At the time of censoring, 19/30 patients were deceased with a median follow up time of 902 days (range, 322-1563 days). Median survival was 508 days (range, 124-1563 days). Age was not a significant factor in overall survival (P=0.718). From the univariate pre-surgical MRI, 5/9 diffusivity, 1/3 perfusion and 2/3 volume measurements were significantly related to overall survival (P<0.05). Post-chemoradiotherapy MRI found 3/9 pharmacokinetic, 2/3 perfusion and 3/3 volume measurements to significantly associated with overall survival. R1 values were not significant predictors of overall survival at either time point, however, 2/3 post-chemoradiotherapy measurements had P-values < 0.1. Measurements of anisotropic diffusion (FA and q) showed no association with overall survival.

The results suggest that preoperative diffusivity measurements contain useful prognostic information relating to overall survival, with lower diffusivity values (more cellular/rapidly proliferating tumours) leading to shorter survival intervals. Anisotropic diffusion parameters showed no associated significance, suggesting that mean diffusivity or even ADC calculated from DWI may be sufficient. Gaussian mixture modelling appears useful for sampling rCBV maps, with the preoperative rCBV P1 subpopulation reaching significance (P=0.035) even when the mean rCBV (TP1) did not (P=0.096). High rCBV values were associated with shorter overall survival. The ability to automatically determine regions of increased perfusion within the total volume of abnormality could have clinical utility and reduce intra-user variability. Interestingly the total volume of tumour abnormality (TUM) and volume of non-enhancing tumour (TUM P0) were significant prognosticators, yet the volume of enhancing tumour on the preoperative scans was not. This may be related to the surgical target and the potential extent of resection.

Following chemoradiotherapy, K^trans, v_e, rCBV and tumour volumes were found to have significant prognostic value (P<0.05) with higher values associated with shorter overall survival. In our cohort, DTI had no prognostic value at this time point following chemoradiotherapy. Significant vascular parameters at this time point were more numerous than at the pre-surgical scan, and may be useful in distinguishing treatment related pseudoprogression/reactive changes from residual tumour and/or progressive disease.

Cox regression analysis identified TP1 MD P0, TP2 K^trans P0, TP2 TUM and TP2 TUM P1 as being key parameters related to survival and confirms the Kaplan-Meier findings that preoperative DTI and post-chemoradiotherapy DCE parameters have added prognostic value to more traditional prognostic features such as tumour volume.

The results from this study suggest that preoperative DTI, DSC and tumour volume measurements all have significant prognostic value prior to treatment in glioblastoma patients. Following chemoradiotherapy, DCE, DSC and tumour volume measurements are better prognostic predictors. Cox regression analysis suggests that preoperative DTI and post-treatment DCE MRI are the most useful sequences in addition to traditional tumour volume measurements.