Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response

Abstract

Cancer cells differ in size from those of their host tissue and are known to change in size during the processes of cell death. A noninvasive method for monitoring cell size would be highly advantageous as a potential biomarker of malignancy and early therapeutic response. This need is particularly acute in brain tumours where biopsy is a highly invasive procedure. Here, diffusion MRI data were acquired in a GL261 glioma mouse model before and during treatment with Temozolomide. The biophysical model VERDICT (Vascular Extracellular and Restricted Diffusion for Cytometry in Tumours) was applied to the MRI data to quantify multi-compartmental parameters connected to the underlying tissue microstructure, which could potentially be useful clinical biomarkers. These parameters were compared to ADC and kurtosis diffusion models, and, measures from histology and optical projection tomography. MRI data was also acquired in patients to assess the feasibility of applying VERDICT in a range of different glioma subtypes. In the GL261 gliomas, cellular changes were detected according to the VERDICT model in advance of gross tumour volume changes as well as ADC and kurtosis models. VERDICT parameters in glioblastoma patients were most consistent with the GL261 mouse model, whilst displaying additional regions of localised tissue heterogeneity. The present VERDICT model was less appropriate for modelling more diffuse astrocytomas and oligodendrogliomas, but could be tuned to improve the representation of these tumour types. Biophysical modelling of the diffusion MRI signal permits monitoring of brain tumours without invasive intervention. VERDICT responds to microstructural changes induced by chemotherapy, is feasible within clinical scan times and could provide useful biomarkers of treatment response.

Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI

“…Future developments should also focus on the histological validation of the quantitative nature of the estimated parameters, even if a limited number of studies compared in‐vivo DW‐MRI parameters to histological features 43,51,52 due to the difficulties in data acquisition 53 and validation. Such validation would allow confirming or adapting the ranges of plausible microstructural markers of the synthetic cellular packings, which were defined in agreement with the available literature 33,34 . However, performing such comparison could be even more challenging for brain tumors treated with proton therapy, for which histological data are not typically acquired, especially at follow‐up times, but direct validation against histopathological imaging 52 should be investigated before the proposed microstructural markers can be employed in any specific clinical application.…”

“…Synthetic substrates comprised of packed cells mimicking tumor tissue microstructure of 100 × 100 × 100 µm 3 volume (Figs. ) were generated by randomly packing ellipsoids with well‐controlled density and plausible geometrical properties (Table II) inferred from the available literature 33,34 . The molecular dynamics algorithm proposed by Donev et al 35 was used to generate the best random configuration of ellipsoids satisfying a chosen set of values for volume fraction (vf), radius (R), and eccentricity.…”

Hybrid PET–MRI Imaging in Paediatric and TYA Brain Tumours: Clinical Applications and Challenges

“…Relative CBV (rCBV) is the most common DSC-MRI metric for evaluating brain tumours. To minimize the error caused by contrast extravasation, known to occur in brain tumours, preloading of the contrast agent along with model-based post-processing leakage correction can decrease both T1 and T2* effects [ 54 , 55 ].…”

The Mathematics of Quasi-Diffusion Magnetic Resonance Imaging

“…These include representing the diffusion signal attenuation as a second moment expansion known as Diffusional Kurtosis Imaging (DKI) [24,25], or as a signal in the experimentally acquired "q-space" from which, via application of the inverse Fourier transform leads to a Mean apparent diffusion propagator (MAP) of molecular displacement [26][27][28] allowing measurement of the length scale of the diffusion environment. This is of interest in clinical imaging as models of the diffusion process that enable quantitative assessment of tissue compartment or cell size can be used to assess pathological change, with potential applications in aiding diagnosis [29,30], predicting disease outcome [31] and monitoring treatment effects [32].…”