Mitotic activity in Glioblastoma correlates with estimated extravascular extracellular space derived from DCE-MRI

Abstract

Purpose

A number of parameters derived from dynamic contrast enhanced MRI (DCE-MRI) and separate histological features have been identified as potential prognosticators in high-grade glioma. This study evaluates the relationships between DCE-MRI derived parameters and histological features in glioblastoma multiforme (GBM).

Methods

28 patients with newly presenting GBM underwent pre-operative imaging (conventional imaging and T₁-DCE-MRI). Parametric maps of IAUC₆₀ (initial area under the contrast agent concentration curve), K^trans (contrast transfer co-efficient) v_e (estimate of volume of the extravascular extracellular space) and v_p (estimate of blood plasma volume) were generated and EnF was calculated. Surgical specimens were used to assess subtype and graded (WHO classification system) and assessed for necrosis, cell density, cellular atypia, mitotic activity and overall vascularity scores. Quantitative assessment of endothelial surface area, vascular surface area and a vascular profile count were made using CD34 immunostaining. The relationships between MRI parameters and histo-pathological features were examined.

Results

High values of K^trans were associated with the presence of frank necrosis (p=0.005). High values of v_e were associated with a fibrillary histological pattern (p<0.01) and with increased mitotic activity (p<0.05). No relationship was found between mitotic activity and histological pattern suggesting the correlation between v_e and mitotic activity was independent of histological pattern.

Conclusion

A correlation between v_e and mitotic activity is reported. Further work is warranted to establish how DCE-MRI parameters relate to more quantitative histological measurements including markers of proliferation and measures of vascular endothelial growth factor (VEGF) expression.

Introduction

Gliomas are the commonest primary cerebral tumour of adulthood. They are histologically classified according to the World Health Organisation (WHO) criteria into tumour grade and subtype (1), which is important for determining appropriate treatment. Within given histological subtypes and tumour grades, a number of additional descriptive histopathological features have been identified as prognosticators including mitotic activity (2-4), microvascular density (MVD) (5,6) and certain vascular patterns (7,8). Both proliferation markers, such as Ki-67 (2-4,7,9,10) and expression of vascular endothelial growth factor (VEGF) (3,5,11-14) have also shown relationships to survival.

Dynamic contrast enhanced-MRI (DCE-MRI) techniques generate a number of parameters that characterize the microvascular environment. The enhancing fraction (EnF) describes the proportion of perfused tumour tissue. CBV and CBF are commonly derived from dynamic susceptibility contrast (DSC-MRI) techniques whilst volume transfer coefficient (K^trans), v_e (fractional volume of the extravascular extracellular space, EES) and v_p (fractional blood plasma volume) can be calculated from T₁-weighted DCE-MRI (15). In glioma, K^trans, v_p, EnF, CBV and CBF have been shown to relate to histological grade and/or subtype of tumour (16-21). In addition, K^trans, EnF and CBV have been identified as potentially grade independent prognosticators (22-25). The number of studies examining the relationship between DCE-MRI parameters and more specific histopathological features in glioma is currently small and predominantly focuses on vascular metrics such as blood volume and flow; however, significant correlations have been described between CBV and MVD (26-29), VEGF expression (27,30), cell density (29), endothelial proliferation (31) and mitotic activity (31) and between CBF and endothelial hyperplasia (32).

More recent studies have focused on the potential of v_e as an imaging biomarker. In glioma, it has shown value in discriminating between histological grade (33,34), but when compared with other potential candidate biomarkers of the extravascular extracellular space volume (Apparent Diffusion Co-efficient, ADC), on a voxel by voxel basis, no correlation was seen between the two metrics, suggesting that these parameters provide independent information about extravascular extracellular space characteristics (35). Two separate studies of glioma patients of various histological grade have both reported significant correlations between both K^trans and v_e and vascular and microvascular density (33,34), but neither study comments upon the relationship with cellular density or mitotic activity. A high field, 7T MRI study of rat xenografts showed an image matched significant negative correlation between v_e and tumor cellularity (36).

We hypothesized that: 1) larger, rapidly growing tumours would show higher mitotic activity as well as high angiogenic activity reflected by K^trans and EnF; and 2) more proliferative tumours would have a higher cellular density and mitotic activity associated with lower values of v_e.

Materials and Methods

Patients

Ethical approval was obtained and all patients gave informed consent. All tumours were histologically confirmed as GBM according to the WHO criteria (1). All imaging was pre-operative and patients received no treatment other than corticosteroids, which were administered for a minimum of 48 hours prior to imaging to allow stabilisation of the effects of steroids on DCE-MRI measures (37). Patients were excluded from the study if they had a history of renal dysfunction or low eGFR (<30 mls/min/1.73m²).

MRI data acquisition

Imaging was performed on a 3 Tesla Philips Achieva system (Philips Healthcare, Best, Netherlands) using a SENSE head coil. DCE-MRI acquisitions were acquired in a sagittal oblique orientation to allow improved definition of the arterial input function (AIF) free from flow related artifact. Three pre-contrast T₁-fast field echo (T₁-FFE; RF-spoiled gradient echo) series (2°, 5°, 16°) were acquired for calculation of baseline T₁ maps (TR 3.5 ms, TE 1.1 ms, slice thickness 4.2 mm, 128 × 128 matrix, FOV 230 mm × 230 mm × 105 mm) in the same geometry. A dynamic, contrast-enhanced acquisition series (TR 3.5 ms, TE 1.1 ms, flip angle 16°, slice thickness 4.2 mm, 128 × 128 matrix, FOV 230 mm × 230 mm × 105 mm) consisting of 100 volumes with temporal spacing of approximately 3.4 seconds followed. A bolus dose of 0.1 mMolkg⁻¹ (body weight) of gadolinium-based contrast agent (Gd-DTPA-BMA; Omniscan, GE Healthcare, Oslo, Norway) was injected at a rate of 3 mls⁻¹, after acquisition of the fifth image volume. Pre and post contrast T₁-weighted imaging sequences (TR 9.3 ms, TE 4.6 ms) were acquired in the same sagittal oblique geometry for definition of volume of interest (VOI) of the whole tumour.

MRI data analysis

An experienced neuroradiologist (SJM) manually defined VOIs for each tumour. The VOI corresponded to the enhancing tumour and all non-enhancing tissue contained within it on the post contrast T₁-weighted images. This technique has previously shown good interobserver agreement (Intraclass Correlation Coefficient > 0.94)(38). Pharmacokinetic analysis was performed on all pixels within the VOI which showed significant enhancement. Parametric maps of K^trans, v_p, v_e and IAUC₆₀ were produced using in-house software (MaDyM – Manchester Dynamic Modelling) and the extended Tofts and Kermode pharmacokinetic model (15). Automated AIFs were generated from an appropriately chosen slice, which included the internal carotid artery (39). Summary statistics for each parameter were generated for enhancing tumour tissue.

For each tumour, EnF (EnF_IAUC60>0) and thresholded EnF (EnF_IAUC60>2.5) were calculated by dividing the enhancing volume (volume of voxels with IAUC₆₀ > 0 mMol.s for EnF_IAUC60>0 and volume of voxels with IAUC₆₀ > 2.5 mMol.s for EnF_IAUC60>2.5) by the total volume of the tumour VOI. The cut off threshold of IAUC₆₀ > 2.5 mMol.s for EnF_IAUC60>2.5 was previously identified as a optimal threshold for allowing the distinction of high from low grade glioma (40).

Histopathological data analysis

Two experienced neuropathologists (DDP and PP) performed the histopathological analysis. Histological specimens were assessed for the following: necrosis (presence or absence of frank and/or geographic necrosis), cell density (3 point grading score), cell atypia (3 point grading score), mitotic activity (number of mitotic figures seen per 10 high power field units), infiltrates (presence or absence of lymphocytes and/or macrophages), tumour vascular pattern (presence or absence of the following features; endothelial hypertrophy and/or hyperplasia, glomeruloid structures, granulation tissue, large vessel density, thrombosis, sclerosed vessels), an overall vascular density score (3 point grading score) and histological pattern (fibrillar, gemistocytic, oligodendrocytes, sarcomatous, giant cells and small cells) in conjunction with standard histopathological subtyping and grading according to the WHO classification criteria (1). Vascular and histological features were not mutually exclusive, therefore more than one feature could be described in a given tumour specimen.

Quantitative measurement of the endothelial surface area (ESA), the vascular surface area (VSA) and the vascular profile count per mm² (VPC) was made using CD34 immunostaining and dedicated image analysis software.

Statistical analysis

Statistical analysis was performed using SPSS (version 15.0, SPSS Inc., Chicago, USA) nonparametric statistical tests. Where histological parameters produce a binary classification (necrosis, infiltrates, vascular patterns and histological patterns) Mann Whitney U tests were performed to test the hypothesis that MRI parameter values did not differ between groups. Where histological features produce categorical scores (cell density, cell atypia and overall vascular score) multivariate analysis of variance (ANOVA) was used to test the hypotheses that MRI parameter values did not differ between groups. Spearman's correlation analysis was performed to assess the relationship between quantitative histological measures (mitotic activity, ESA, VSA and VPC) and MRI parameters and to identify correlation between the individual MRI measurements. For Mann Whitney U and ANOVA testing a result was considered to be significantly different if p<0.01, given the number of variables assessed. For Spearman’s correlation analysis significance was taken as <0.05.

Results

28 untreated newly presently GBM were included in the study (10 female, age range 38 - 76 years, mean 60 years). Histological specimens were obtained from 12 biopsies and 16 surgical debulkings. Results of statistical analyses for comparisons of histological and MRI measures are summarized in Table 1.

Table 1.

Summary of statistical comparisons between histopathological and DCE-MRI measures. Mann Whitney U tests were used for histological parameters which produce a binary classification (necrosis, infiltrates, vascular patterns and histological patterns) and multivariate analysis of variance (ANOVA) was used for histological features which produce categorical scores (cell density, cell atypia and overall vascular score). Spearman's correlation analysis was performed to assess the relationship between MR derived parameters and quantitative histological measures (mitotic activity, ESA, VSA and VPC). Significance levels are shown and Spearman’s ρ is shown for correlation analyses.

		Ktrans (min−1)	ve (unitless)	vp (unitless)	EnFIAUC60>0 (unitless)	EnFIAUC60>2.5 (unitless)
Necrosis	Frank	0.005**	0.694	0.694	0.264	0.088
	Geographic	0.495	0.655	0.466	0.384	0.796
Cell density		0.392	0.165	0.953	0.455	0.169
Cell atypia		0.958	0.629	0.654	0.630	0.149
Mitotic activity		0.400 ρ=0.166	0.012* ρ=0.470	0.678 ρ=−0.082	0.729 ρ=−0.069	0.744 ρ=0.065
Infiltrates	Lymphocytes	0.353	0.147	0.911	0.629	0.393
	Macrophages	0.629	0.738	0.683	0.629	0.970
Tumour vascular patterns	Hypertrophy	0.592	0.592	0.153	0.422	0.212
	Hyperplasia	0.569	0.787	0.197	0.787	0.418
	Glomeruloid	0.394	0.504	0.504	0.134	0.596
	Granulation tissue	0.325	0.896	0.896	0.015	0.076
	Large vessel	0.164	0.164	0.137	0.848	0.502
	Thrombosis	0.472	0.924	0.632	0.472	0.811
	Sclerosed	0.293	0.095	0.155	0.951	0.122
Histological patterns	Fibrillar	0.325	0.007**	0.325	0.793	0.212
	Gemistocytic	0.937	0.442	0.614	0.730	0.353
	Oligodendro	0.155	0.353	0.757	0.155	0.155
	Sarcomatous	0.929	0.789	0.929	0.372	0.592
	Giant Cells	0.697	0.697	0.976	0.787	0.610
	Small Cells	0.970	0.683	0.911	0.738	0.738
Overall vascular score		0.125	0.759	0.385	0.214	0.278
Vascular measures	ESA Ratio	0.839 ρ=0.041	0.809 ρ=−0.049	0.493 ρ=−0.0138	0.344 ρ=−0.189	0.799 ρ=−0.051
	VSA Ratio	0.919 ρ=0.020	0.855 ρ=−0.037	0.495 ρ=−0.137	0.298 ρ=−0.208	0.782 ρ=−0.056
	VPCmm−2	0.224 ρ=0.242	0.626 ρ=−0.098	0.119 ρ=0.307	0.065 ρ=0.360	0.071 ρ=0.353

^*significance taken as p < 0.05 (Spearman’s correlation analysis)

^**significance taken as p < 0.01(Mann Whitney U and ANOVA)

The presence of frank necrosis was associated with significantly higher values of K^trans (p<0.01, Figure 1). Significantly higher values of v_e were seen in the presence fibrillary histology (estimated p<0.01, Figure 1). A positive correlation was found between v_e and mitotic activity (p<0.05, ρ =0.470, Figure 2). No relationship was seen between mitotic activity and any of the descriptive or semi-quantitative histology measures, suggesting the relationship between v_e and mitotic activity is independent of the relationship between v_e and the presence of fibrillar histology. No correlation was observed between v_e and cell density.

Figure 1.

a) Boxplot of fibrillar histology and ve (estimated p = 0.007). b) Example histological specimens showing tumours without and c) with the presence of fibrils (small fibres measuring approximately 1 mm, black arrows). H&E stain × 40 magnification. d) Boxplot of frank necrosis and Ktrans (estimated p = 0.005).

Figure 2.

Scatter plot of mitotic activity versus ve (p = 0.012, r = 0.470), colour coded to demonstrate separate scores of cell density measures.

Cross correlations between individual MRI parameters are summarised in Table 2. Positive correlations were found between K^trans and all other MRI parameters. Significant correlations were present between v_e and K^trans (p<0.05 and ρ=0.450) and between v_p and EnF. Mitotic activity did not correlate with any quantitative vascular measure, but cross correlation was seen across all three histological vascular metrics (Table 3). No other significant relationships were identified between histological measures.

Table 2.

Cross correlations of MRI parameters. Significance levels and Spearman’s ρ are shown.

	Untreated GBM (n = 28)
	v e	v p	EnFIAUC60>0	EnFIAUC60>2.5
K trans	0.016* ρ=0.450	0.006** ρ=0.507	0.005** ρ=0.513	<0.001** ρ=0.784
v e		0.105 ρ=0.313	0.888 ρ=0.028	0.059 ρ=0.361
v p			0.02* ρ=0.437	<0.001** ρ=0.620
EnFIAUC60>0				<0.001** ρ=0.825

^*significance taken as p < 0.05

^**significance taken as p < 0.01

Table 3.

Cross correlations of quantitative histological measures. Significance levels and Spearman’s ρ are shown.

	Untreated GBM (n = 28)
	ESA ratio	VSA ratio	VPC/mm2
Mitotic activity	0.098 ρ=0.325	0.066 ρ=0.358	0.737 ρ=0.068
ESA ratio		<0.001** ρ=0.993	0.04* ρ=0.530
VSA ratio			0.04* ρ=0.532

^*significance taken as p < 0.05

^**significance taken as p < 0.01

Discussion

Whilst there has been considerable interest in the development of imaging biomarkers for use in clinical trials, the majority of DCE-MRI studies have focused on identifying correlates of vascularity and angiogenesis (41). This study identified an unexpected positive correlation between v_e, a parameter thought to reflect EES volume, and mitotic activity. No relationships were identified between previously described DCE-MRI prognosticators, K^trans and v_p, and mitotic activity, nor between vascular patterns and DCE-MRI measures.

The parameter v_e is often overlooked in DCE-MRI studies. In cerebral tumours, it has potential to distinguish intra- from extra-axial tumours (42,43), and exhibits increased values with increasing tumour grade (33,34,42,44). It has also demonstrated sensitivity in identifying changes in response to treatment with corticosteroids, with decreases in v_e, presumably reflecting a reduction in interstitial oedema (45,46). In a study of vestibular schwannomas, meningiomas and gliomas, no direct measure of the EES or cell density measure was made but v_e values in schwannoma were noted to be significantly larger than in glioma, where cell density is higher (43). In a study comparing v_e with ADC in glioma, no relationship was found between the two metrics suggesting that these parameters may provide independent information about the extravascular extracellular space (35). This may reflect the existence of separate extravascular leakage compartments characterised by fast and contrast agent exchange, possibly reflecting the presence of slow leakage / diffusion into and out of necrotic tumour spaces (42,47,48). Previous studies have also reported correlations between MVD and both v_e and K^trans (33,34). We did not find this correlation in our study but there was a cross correlation between the DCE-MRI parameters v_e and K^trans and raising the possibility that the correlation reported in the previous studies reflects the relationship of v_e with K^trans rather than MVD. A previous rat xenograft study has reported correlation between v_e and cellular density (36), but our current study found no such relationship. Histological measures of cell density are often a count of the number of cell nuclei per unit area and without quantification of the amount of tissue the cells occupy, thus an increase in histological cell density may reflect an increase in number of cells, but not necessarily a decrease in extra-vascular, extra cellular space if the cells are small in size. It should be noted that there are potential modelling problems associated with the calculation of v_e. Clearly it can be estimated only in perfused tissue where there is significant leakage of contrast agent (35). This means that summary statistics presented in this and other studies reflect only perfused tissue with contrast agent leakage, in contrast to diffusion-weighted imaging where high ADC values are seen in association with necrosis. We have specifically excluded non-perfusing tissue from estimates of summary statistics, which could otherwise produce artificially low values of v_e. In addition, the relatively low dynamic sampling duration (6 minutes) will affect v_e estimates to some degree. First, model fitting errors (assuming the model is correct) will result from undersampling, but modelling studies suggest that these fitting errors are likely to be very small (49). Second, short sampling times will lead to relative under-representation of slow tissue exchange compartments, which would tend to reduce the impact of necrotic tissue on v_e estimations as described in (42,47,48). This implies that tumours with greater necrosis would have shown higher v_e values if sampling had continued for a longer time and that the measured values of v_e in this study are more likely to reflect EES fraction in viable tissue.

The positive correlation between v_e and mitotic activity is surprising. Tumours with larger v_e values exhibited more mitotic activity, the inverse of what one might expect (and the inverse of our initial hypothesis), whereby more proliferative tumours would be more densely packed with cells. Neither mitotic activity nor v_e related to the presence of necrosis, although the lack of relationship between v_e and necrosis may in part reflect the relatively low dynamic collection period (vide supra). These observations suggest that the size of the EES in perfused enhancing tumour tissue is truly related to mitotic rate and not simply a reflection of elevated measures of v_e due to increased necrosis in rapidly proliferating tumours.

This is initially counter-intuitive. It is well known that in normal developing tissues mitotic activity is higher in areas of low cell packing due to inhibition of proliferation in response to cell to cell contact, a process known as contact inhibition of proliferation (CIP) in developing normal tissue (50). No similar relationship has been described in malignant tissues and loss of contact inhibition of mitosis is one of the hallmarks of the cancer cell (51). This leads to the hypothesis, stated in the introduction, that rapidly proliferating tumours will continue to proliferate leading to increased cell density and decreased size of the EES and consequently of v_e. We have no evidence to explain why the observed relationship should exist. One possible explanation may be that tumours with high cellular density still have impaired responses to tumoural growth factors despite loss of CIP (52). Another possible explanation is that tumours with short mitotic cycles are characterised by reduced time for cellular maturation resulting in smaller cells and reduced cell packing. Whatever the underlying biological mechanism this finding appears particularly interesting and requires further study.

The ability to obtain an MRI-based imaging biomarker of mitotic activity and/or tumour cell proliferation is highly desirable. The findings presented here may be tumour specific or reflect an unrecognised phenotype, but the possibility that v_e may be a potential marker of mitotic activity merits further evaluation.

The data also demonstrated a positive relationship between tumour cell fibrillarity within the histological specimens and higher v_e, although the numbers were very small (n=4). Fibrillar cell processes are cell extensions containing cytoplasm, which are surrounded by cell membranes. These are visible through a microscope and allow tumour astrocytes to be recognised as “fibrillar”. A less cell-dense arrangement due to such cell extensions and/or intercellular oedema facilitate its recognition via light microscopy. Thus, the ability to identify fibrillar cell processes suggests that the tumour cells are more loosely packed or there is localized extracellular oedema. No relationship was identified between fibrillar histology and mitotic activity indicating that the relationships between v_e and these measures are independent.

The majority of previous work evaluating the relationship between specific histological features and DCE-MRI has focused predominantly on CBV derived from DSC techniques with significant relationships seen between CBV and MVD (26-29), VEGF expression (27,30), cell density (29), endothelial proliferation (31) and mitotic activity (31). No such relationships were identified between these latter three measures and v_p in this study. A study by Ludemann et al compared a variety of MR imaging techniques with H₂¹⁵O-PET for measuring perfusion (53) and found that whilst both DSC and T₁-weighted DCE-MRI techniques correlated with the gold standard H₂¹⁵O-PET measure, only borderline correlation was seen between the DSC techniques and the T₁-weighted technique, whereby DSC derived blood volumes were generally lower than those derived from the T₁-weighted DCE-MRI technique. This may account for the failure of v_p to relate to any of the histological measures in this study.

The major limitation of the present work is the lack of stereotactic image matched histological specimens and therefore correlation between the histology and the DCE-MRI measurements at a local level cannot be made. GBMs are notoriously heterogeneous tumours and histological analysis of small tumour specimens may lead to under-grading of a tumour if the sample is not a true reflection of the tumour as a whole. At the time of the study, imaged matched histological samples were not obtained and there was no concurrent post-operative imaging performed which could have helped to identify the site of the histological sample. All histological specimens in this study did confirm the diagnosis of GBM and therefore are considered representative samples. Previous non image registered studies comparing v_e with histopathological vascular measures have been reported but these have been performed with a selection of 3-4 small ROIs, taking the maximal value of v_e and K^trans (33,34), which is unlikely to provide a representation of the tumour as a whole. In our current study, for the DCE-MRI measures K^trans, v_e, and v_p, median values from whole tumour VOIs were used. Studies using whole tumour VOIs have previously identified significant differences in DCE-MRI parameters between tumour grades (17), and been shown to convey important potential prognostic information (25). Thus a comparison between small histological samples and DCE-MRI parameters from whole tumour VOIs seems reasonable. Further work is required to confirm that the correlations identified in this current study hold true for image matched stereotactic samples. In addition, some histological features such as Ki-67 and VEGF expression, which have previously shown correlation with DSC-MRI derived CBV (20,26-30), were not available.

Conclusion

The DCE-MRI derived measure, v_e, has been identified as a potential correlate of mitotic activity in GBM. Whilst this is an interesting result, our understanding of the biological mechanisms responsible for this possible relationship is limited. Further work with the correlation of v_e to more precise measures of cell density, additional markers of cellular and vascular proliferation, and measures of VEGF expression is warranted.

Abbreviations

DCE-MRI

dynamic contrast enhanced MRI

EES

extravascular extracellular space

EnF

enhancing fraction

ESA

endothelial surface area

¹⁸F-FLT

¹⁸F-labeled 3′-deoxy-3′-fluorothymidine

GBM

glioblastoma multiforme

IAUC₆₀

initial area under the contrast agent concentration curve

K^trans

contrast transfer coefficient

MVD

microvascular density

v_e

volume of the EES per unit tissue volume

VEGF

vascular endothelial growth factor

v_p

volume of the blood plasma per unit tissue volume

VPC

vascular profile count per mm² (VPC)

VSA

vascular surface area

WHO

World Health Organisation