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.

Introduction: To combine numerical simulations, in vitro and in vivo experiments to evaluate the feasibility of measuring diffusion exchange across the cell membrane with diffusion exchange spectroscopy (DEXSY). Methods: DEXSY acquisitions were simulated over a range of permeabilities in nerve tissue and yeast substrates. In vitro measurements were performed in a yeast substrate and in vivo measurements in mouse tumor xenograft models, all at 9.4 T. Results: Diffusion exchange was observed in simulations over a physiologically relevant range of cell permeability values. In vitro and in vivo measures also provided evidence of diffusion exchange, which was quantified with the Diffusion Exchange Index (DEI). Conclusions: Our findings provide preliminary evidence that DEXSY can be used to make in vivo measurements of diffusion exchange and cell membrane permeability. K E Y W O R D S cell membrane permeability, DEXSY, Diffusion exchange, FEXSY

There has been slow progress in the development of new therapeutic strategies for treating brain tumours, partly because assessment of treatment response is difficult and largely reliant on simple bi-dimensional measurements of MRI contrast-enhancing regions. Hence, there is a clinical need to develop improved imaging techniques for monitoring treatment response. In this study, we evaluate VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) MRI in mouse glioblastomas for the quantification of tumour microstructure and assessment of response to Temozolomide (TMZ) chemotherapy, and, we investigate the feasibility of applying VERDICT MRI in a range of human gliomas.VERDICT MRI detected response to TMZ earlier than structural and apparent diffusion coefficient (ADC) measurements. A significant reduction in the cell radius parameter was detected three days earlier than ADC and six days earlier than structural MRI. Histological analysis showed the same trend as VERDICT of decreased intracellular volume fraction in the TMZ-treated mice. Vascular volume fraction was not altered by TMZ, which was consistent with optical projection tomography measurements. In patients, glioblastoma compartmental volume fractions showed good agreement with mouse glioblastoma parameters. The VERDICT parameters varied across the human gliomas, with raised intracellular volume fraction in the oligodendrogliomas and elevated cell radius in both lowgrade tumours subtypes. In conclusion, our results suggest that VERDICT MRI is more sensitive at detecting TMZ response than structural or ADC measurements. In patients, VERDICT is feasible within clinical scan times, and performed best at characterising glioblastoma. Further optimisation should improve assessment of different glioma subtypes.

1Artificial neural networks are being widely implemented for a range of different biomedical 2 imaging applications. Convolutional neural networks are by far the most popular type of deep 3 learning architecture, but often require very large datasets for robust training and evaluation. 4We introduce deep learning diffusion fingerprinting (DLDF), which we have used to classify 5 diffusion-weighted magnetic resonance imaging voxels in a mouse model of glioblastoma 6 (GL261 cell line), both prior to and in response to Temozolomide (TMZ) chemotherapy. We 7show that, even with limited training, DLDF can automatically segment brain tumours from 8 normal brain, can automatically distinguish between young and older (after 9 days of growth) 9 tumours and that DLDF can detect whether or not a tumour has been treated with 10 chemotherapy. Our results also suggest that DLDF can detect localised changes in the 11 underlying tumour microstructure, which are not evident using conventional measurements 12 of the apparent diffusion coefficient (ADC). Tissue category maps generated by DLDF 13 showed regions containing a mixture of normal brain and tumour cells, and in some cases 14 evidence of tumour invasion across the corpus callosum, which were broadly consistent with 15 histology. In conclusion, DLDF shows the potential for applying deep learning on a pixel-wise 16 level, which reduces the need for vast training datasets and could easily be applied to other 17 multi-dimensional imaging acquisitions. 18 19

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