sofie van cauter uwe himmelreich, stefaan van gool, stefan sunaert

“Advanced MR imaging in diagnosis, treatment planning and therapy monitoring in gliomatous brain

tumours: review of the literature”

Sofie Van CauterUwe Himmelreich, Stefaan Van Gool, Stefan Sunaert

Advanced multimodal MRI in gliomas

1. Introduction

2. Review of the literature

3. Scope of our research

Medical Imaging Research Center July 2010

1. Introduction

Gliomas (arise from neuroectodermal glial cells): 7/100 000/year

Astrocytomas (pilocytic vs diffuse) Oligodendrogliomas Ependymomas Gangliogliomas

Low grade (WHO gr I and II) High grade (WHO gr III and IV)

Gr IV: glioblastoma multiforme (primary – secondary; 4/100 000/year)

Treatment: “watchfull waiting” (+ biopsy) <-> debulking , radiotherapy, chemotherapy new evolving therapies immune therapy, targeted therapy,…..

Overall bad prognosisLGG: the 5-year survival rate 65-80%, the 10-year survival: 20-45% (heterogeneous group)GBM: overall survival 15 m, at the time of relapse 100% mortality after 1.5 y


1. Introduction

How to asses?

NEUROIMAGING

• Computed tomography• Magnetic resonance imaging

• Positron emission tomogrpahy• Single photon emission computed tomography

• Diffuse optical imaging• Event-related optical signal• Electroencephalography• Mangetoencephalography

Radiology

Nuclear medecine


1. Introduction

Anatomical imaging techniques Functional imaging techniques

Magnetic resonance imaging

* diffusion weighted imaging

* diffusion tensor imaging, diffusion kurtosis imaging

* perfusion weighted imaging (DCE)

* MR spectroscopy

* functional MR imagingMedical Imaging Research Center July 2010

* T1 +/- contrast administration, T2, FLAIR

1. Introduction


Diffusion weighted imaging – Diffusion tensor imaging – Diffusion kurtosis imaging

- Brownian molecular motion diffusion - In biological tissue, restriction of “mobility” due to tissue cellularity and cell mebrane integrity- MR derived parameters: ADC, FA, MK, ……..

+ =

1. Introduction


Perfusion weighted imaging

- Perfusion-weighted MRI is a non-invasive imaging method for quantification of vascular properties.

- Dynamic susceptibility contrast magnetic resonance imaging (DSC-MR) is acquired by repetitive imaging with high temporal resolution during the injection of Gd-Based contrast agent

- Derived parameters: rCBV, rCBF, MTT,…..

1. Introduction


MR spectroscopy

- Detection of mobile H containing metabolites.- MRS provides information regarding the composition and spatial distribution of cellular metabolites- Variable acquisition techniques: CSI, SV, long TE, short TE.

Water signal

Membrane turnover

Energy metabolism

Neuronal marker

1. Introduction


MR spectroscopy

Different TE

TE: 35ms TE:144 ms

Detection of pathology SV vs CSI

1. Introduction

What to asses in brain neoplasms with neuroimaging techniques?

- diagnosis - grading - progression / relapse after treatment - treatment effects



1. Introduction




2. Review of the literature 2.1 Advanced MRI in diagnosing gliomas

“ Distinction between high-grade gliomas and solitary metastases using peritumoral 3T

magnetic resonance spectroscopy, diffusion and perfusion imaging ”

Chan Chiang I. et al.Neuroradiology 2004



Subjects 26 patients: 14 high grade gliomas and 12 metastases presurgical histopathology confirmed

Methodology - Prospective study / biopsy or surgical resection histopathological confirmation- 3T; conventional MR, MRS, DWI 12 pts PWI

PWI: rCBV in 3 regions (tumoral region, peritumoral edema and NAPWM)CSI: Cho/Cre and NAA/Cre (maximal values in 3 regions)DWI: ADC maps

Results - PWI: rCBV is significantly higher in peritumoral edema of HGG

- CSI: Cho/Cre is significantly higher in peritumoral edema of HGG

- DWI: ADC in peritumoral edema and contrast enhancing areas of metastases significantly higher than in HGG


“ Distinction between high-grade gliomas and solitary metastases using peritumoral 3T magnetic resonance spectroscopy, diffusion and perfusion imagings ”, Chan Chiang I. et al., Neuroradiology 2004


MR spectroscopy

Perfusion weighted imaging

Diffusion weighted imaging



CSI, TE: 270 TR: 1500ms

rCBV map

ADC maps, TE: 100 ms TR: 12000ms, b-value: 1000 mm/s²


Conclusion:

Perfusion-weighted MRI, diffusion weighted MRI and MR spectroscopy (Cho/Cr) in the peritumoural region can be used to demonstrate differences in solitary metastases and high-grade

gliomas. The intratumoural rCBV, Cho/Cr, NAA/Cr and peritumoural NAA/Cr do not differ statistically from those seen with metastases



2. Review of the literature 2.2 Advanced MRI in grading gliomas

“ Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural

differences”

Raab P. et al.Neuroradiology 2010



Subjects 5 grade II astrocytomas13 grade III astroctyomas16 grade IV glioblastoma multiforme

Methodology - Prospective study / surgery within three weeks histopathological confirmation- 3T 6 b-values (0, 500, 1000, 1500, 2000 and 2500 sec /mm²), 30 directions each- segmentation of the most solid part of the tumour on T2w-images- average MK, average FA, average ADC for every region of interest; normalized MK, FA and ADC

Results - Average MK and normalized MK values increased with higher tumour grades significant between gr II, III and IV

- Average and normalized ADC values decreased with higher tumour grades significant between gr III and IV, NOT between gr II and III

- Average FA values: no difference between gr II and III; slightly increase in GBM without statistical significance


“ Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences”, Raab P. et al.; Neuroradiology 2010


“The data demonstrate significantdifferences in MK values among gliomas of different WHO grades”




Conclusion:

There are significant differences in mean DK between glioma grades II through IV, thereby showing a better separation between

tumour grades by mean DK than by conventional DTI measurements.

This new technique potentially can be used as another non-invasive biomarker for tumour grading.




“ Nosological imaging of the brain: segmentation and classification using MRI

and MRSI”

Luts J. et al.NMR in Biomedicine 2009



Subjects 24 patients (diffuse astrocytoma, oligoastrocytoma, oligodendroglioma, glioblastoma and meningioma) and 4 healthy volunteers selected from a database histopathological confirmed

Methodology - 1.5T short echo time spectra 2D STEAM

- Training data set for pattern recognition: several voxels from the tumor area (HP confirmed) and normal appearing tissue

- Segmentation of parenchym tissue (tumour, edema, NAP) on anatomical images.

- Coregistration with MRSI data.

- Classification based on pattern recognition in order to provide information on the tissue type TWO STEP SEGMENTATION-CLASSIFICATION METHOD

Results - The method proposed in the paper is flexible: any classifier can be integrated.

- Probability maps


“ Nosological imaging of the brain: segmentation and classification using MRI and MRSI” Luts J. et al., NMR in Biomedicine 2008




NAP CSF

Gr II Gr III

Gr IVmeningiomaGlioblastoma multiforme

LEGEND: Light blue: WM; dark blue: GM; green: CSF; yellow gr II; orange: gr III glioma; dark red: GBM

T1 T2 FLAIR T1 +

NOS IM




Glioma grade II

LEGEND: Light blue: WM; dark blue: GM; green: CSF; yellow gr II; orange: gr III glioma; dark red: GBM

Glioma grade II/III

T1 T1 T2T2 PDPD T1 +T1 +

NOS IMNOS IM




Probability maps and contour plots

LEGEND: • the lighter the probability map, the higher the probability for a

certain tissue type.• the blue contour lines show higher gr III probabilities, as opposed by

the red lines.




Conclusion:

A new method to generate nosologic images of the brain by combining MRI and MRSI in a two-step approach. First, abnormal tissue is segmented. Next, the abnormal tissue is classified using

pattern recognition. Class probabilities are generated for the diverse tissue types.

2. Review of the literature 2.3 Advanced MRI in treatment FU/prognosis


“ Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma””

Tsien C et al.J Clin Onc 2010



Subjects - 27 patients with HGG receiving concurrent chemoradiotherapy (20 pts previous surgery)

Methodology - prospective study- MRI prior to R/, week 1 and week 3. (DCE-MRI)- normalized rCBV and rCBF maps- segmentation of GTV- the differences between serial rCBV/rCBF maps calculated for each voxel within the GTV pre and on week 3.-T on Δ rCBV > 1.2 or < -1.2 (PRM: parametric response mapping)- 1 m post-treatment, anatomical MR to determine response treatment.

Results - No significant results when looking at rCBV or rCBF.

- No significant results when looking at baseline mean rCBF and mean rCBV in PD and PP. Statistically significant results when looking at baseline mean rCBV between pts with SD and PD.

- Significant differences in change of rCBV between PD en PP

“ Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma”, Tsien C et al., J Clin Onc 2010




Pseudoprogression: transient increase in contrast enhancement without evidence for tumour recurrence (hypothesis: inflammatory response to treatment)

LEGEND: red: significant increase in rCBV, blue: significant decrease; green: unchanged

In HGG, * tumour vasculature is compromised due to rapid tumour

growth * angiogenesis leading to a high density of leaky

and immature vessels * the tumour core is characterized by regression and low

vessel density

PD

PP




Conclusion:

“Parametric response maps applied to parameters determined by perfusion-weighted MRI are a potentially important biomarker in

distinguishing pseudoprogression and progressive disease in patients with high grade glioma receiving concurrent

chemoradiation.”

2. Review of the literature 2.4 Advanced MRI in detecting recurrence

“ Predicting patterns of glioma recurrence using diffusion tensor imaging”

Price SJ et al.Eur Radiol 2007



Subjects 8 grade II astrocytomas5 grade III astroctyomas12 grade IV glioblastoma multiforme

Methodology - Restrospective study , DTI + follow-up imaging (6 in WW, 19 in R/)- 3T, 12 directions with 6 b-values each (0-1570 mm/s²)- eigenvalues isotropic component p, anisotropic component q maps- delineate areas with reduced q and abnormalities in p

--> Compare with images at the time of recurrence on conventional imaging

Results - DIFFUSE PATTERN OF DTI ABNORMALITY: The p abnormality exceeded diffusely beyond the q abnormality. Tumour recurrence showed a generalized increase in the size of the tumour

- LOCALISED PATTERN OF DTI ABNORMALITY: Tumour regrowth occured in the direction where the isotropic abnormality p exceeded the anisotropic abnormality q

- MINIMAL PATTERN OF DTI ABNORMALITY: no evidence of tumour regrowth (one exception)


“ Predicting patterns of glioma recurrence using diffusion tensor imaging”, Price SJ et al., Eur Radiol 2007


P isotropic abnormalityQ anisotropic abnormality

DIFFUSE PATTERN OF DTI ABNORMALITY

LOCALISED PATTERN OF DTI ABNORMALITY

MINIMAL PATTERN OF DTI ABNORMALITY



T2 T2

T2

T2

T2

T2

T2

b0

b0

b0


Conclusion:

Diffusion tensor imaging can predict patterns of tumour recurrence. Looking at patterns from either tumour infiltration or occult tumour, not seen on conventional images may be helpful in

directing surgical treatments, guiding biospies and directing local chemotherapy and radiotherapy treatments.




1. Introduction





To monitor treatment effects in immune therapy for high grade gliomas

APPLICATION OF ADVANCED MR TECHNIQUES

- To differentiate antitumour immune respons from tumour relapse/progression tool to assess vaccine efficacy

- To propose criteria to distinguish responders from non-responders in an early stage.

Immune Response Tumour Relapse



Immune therapy



* Translational research program in KU/UZ Leuven

proof of principle experiments to demonstrate

immunogenicity of patient derived mature DCs loaded

with autologous tumour lysate

pre-clinical in vivo experiments in a murine

orthotopic glioma mouse model

phase I/II clinical trials for relapsing patients as

solitary treatment and a phase II trial for patients

with newly diagnosed GBM for whom immunotherapy is integrated in the current

multimodal treatment

laboratory analyses of patient samples



pre-clinical in vivo experiments in a murine

orthotopic glioma mouse model

phase I/II clinical trials for relapsing patients as

solitary treatment and a phase II trial for patients

with newly diagnosed GBM for whom immunotherapy is integrated in the current

multimodal treatment

Macrophage labeling with USPIO

MR spectroscopy and DTI in the mouse model

MR spectroscopy and DKI/DTI in a longitudinal patient study

NEUROIMAGING

MoSAIC KUL

Department of radiology UZL



CURRENT STATUS

MR spectroscopy and DKI/DTI in a longitudinal patient study10 à 15 patients with GBM treated with immune therapy

monthly follow-upanatomical imaging: (T2, FLAIR, T1 +/- contrast)

advanced techniques : PWI, DKI, CSI

PILOT EXPERIMENT 1: reproducibility of an optimized CSI protocol

PILOT EXPERIMENT 2: DKI and MRS in LGG and HGG in grading and the characterization of tumour infiltration in gliomatous brain tumours.



Acknowledgments

MIRC:Caroline SageSilvia KovacsJudith VerhoevenSabine DeprezThijs DhollanderJanaki RangarajanRon PeetersWim Van HeckeStefan Sunaert

MoSAIC:Cindy LetenAshwini AtreJesse TrekkerGreetje VandeveldeTom DresselaersUwe Himmelreich

ESAT:Anca Croitor

Maria Isabel OsorioJan Luts

Diana SimaSabine Van Huffel

Department of radiology UZL:Guido Wilms

Philippe DemaerelRaymond Oyen

Guy Marchal

Thank you for the attention ! Questions?

sofie van cauter uwe himmelreich, stefaan van gool, stefan sunaert

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