A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo (MCMC) simulation

The focus of this study is the development of a statistical modelling procedure for characterising intra-tumour heterogeneity, motivated by recent clinical literature indicating that a variety of tumours exhibit a considerable degree of genetic spatial variability. A formal spatial statistical model has been developed and used to characterise the structural heterogeneity of a number of supratentorial primitive neuroecto-dermal tumours (PNETs), based on diffusionweighted magnetic resonance imaging. Particular attention is paid to the spatial dependence of diffusion close to the tumour boundary, in order to determine whether the data provide statistical evidence to support the proposition that water diffusivity in the boundary region of some tumours exhibits a deterministic dependence on distance from the boundary, in excess of an underlying random 2D spatial heterogeneity in diffusion. Tumour spatial heterogeneity measures were derived from the diffusion parameter estimates obtained using a Bayesian spatial random effects model. The analyses were implemented using Markov chain Monte Carlo (MCMC) simulation. Posterior predictive simulation was used to assess the adequacy of the statistical model. The main observations are that the previously reported relationship between diffusion and boundary proximity remains observable and achieves statistical significance after adjusting for an underlying random 2D spatial heterogeneity in the diffusion model parameters. A comparison of the magnitude of the boundary-distance effect with the underlying random 2D boundary heterogeneity suggests that both are important sources of variation in the vicinity of the boundary. No consistent pattern emerges from a comparison of the boundary and core spatial heterogeneity, with no indication of a consistently greater level of heterogeneity in one region compared with the other. The results raise the possibility that DWI might provide a surrogate marker of intra-tumour genetic regional heterogeneity, which would provide a powerful tool with applications in both patient management and in cancer research

Diffusion MRI for characterising childhood brain tumours

Magnetic resonance imaging (MRI) is widely used both in the clinic and as a research tool in the management of brain tumours. While most studies focus on adult tumours, which have a higher incidence than those in children, paediatric brain tumours differ widely in terms of biology and treatment management. Furthermore, as a non-invasive and non-ionising imaging tool, MRI is used in the diagnosis, prognosis and assessment of treatment response of such tumours. This work focuses on diffusion MRI to study childhood brain tumours. The thesis is divided into three main parts: a reproducibility study of diffusion MRI parameters in order to answer the question of whether clinical imaging may be used interchangeably across multiple-centres to combine data from different institutions; a study on a tumour border diffusion measure as a prognostic biomarker in children with embryonal brain tumours – the apparent transient coefficient in tumour (ATCT); and a study analysing the functional diffusion map (fDM) as a tool for assessing treatment response in paediatric brain tumours. Diffusion MRI has been shown to have a good reproducibility and thus data from multiple centres and scanners can be combined in order to analyse clinical data for patients treated at different institutions; particularly where data for specific tumour types would otherwise be limited. In addition, ATCT has been shown to be a useful prognostic biomarker in children with embryonal brain tumours. Finally, while the fDM may be beneficial in assessing treatment response, the underlying biology of both tumour and healthy tissue needs to be carefully considered, and in particular, areas of necrosis, tumour grade and change in tumour size need to be taken into account. In conclusion, diffusion MRI is a valuable tool in the management of childhood brain tumours, with multi-centre studies paving the way for further research and validation of biomarkers

Intraoperative ultrasound in brain tumor surgery: A review and implementation guide.

Accurate and reliable intraoperative neuronavigation is crucial for achieving maximal safe resection of brain tumors. Intraoperative MRI (iMRI) has received significant attention as the next step in improving navigation. However, the immense cost and logistical challenge of iMRI precludes implementation in most centers worldwide. In comparison, intraoperative ultrasound (ioUS) is an affordable tool, easily incorporated into existing theatre infrastructure, and operative workflow. Historically, ultrasound has been perceived as difficult to learn and standardize, with poor, artifact-prone image quality. However, ioUS has dramatically evolved over the last decade, with vast improvements in image quality and well-integrated navigation tools. Advanced techniques, such as contrast-enhanced ultrasound (CEUS), have also matured and moved from the research field into actual clinical use. In this review, we provide a comprehensive and pragmatic guide to ioUS. A suggested protocol to facilitate learning ioUS and improve standardization is provided, and an outline of common artifacts and methods to minimize them given. The review also includes an update of advanced techniques and how they can be incorporated into clinical practice

Stability and reproducibility of co-electrospun brain-mimicking phantoms for quality assurance of diffusion MRI sequences

Grey and white matter mimicking phantoms are important for assessing variations in diffusion MR measures at a single time point and over an extended period of time. This work investigates the stability of brain-mimicking microfibre phantoms and reproducibility of their MR derived diffusion parameters. The microfibres were produced by co-electrospinning and characterized by scanning electron microscopy (SEM). Grey matter and white matter phantoms were constructed from random and aligned microfibres, respectively. MR data were acquired from these phantoms over a period of 33 months. SEM images revealed that only small changes in fibre microstructure occurred over 30 months. The coefficient of variation in MR measurements across all time-points was between 1.6% and 3.4% for MD across all phantoms and FA in white matter phantoms. This was within the limits expected for intra-scanner variability, thereby confirming phantom stability over 33 months. These specialised diffusion phantoms may be used in a clinical environment for intra and inter-site quality assurance purposes, and for validation of quantitative diffusion biomarkers

Optimization of deep learning methods for visualization of tumor heterogeneity and brain tumor grading through digital pathology

Background: Variations in prognosis and treatment options for gliomas are dependent on tumor grading. When tissue is available for analysis, grade is established based on histological criteria. However, histopathological diagnosis is not always reliable or straight-forward due to tumor heterogeneity, sampling error, and subjectivity, and hence there is great interobserver variability in readings. Methods: We trained convolutional neural network models to classify digital whole-slide histopathology images from The Cancer Genome Atlas. We tested a number of optimization parameters. Results: Data augmentation did not improve model training, while a smaller batch size helped to prevent overfitting and led to improved model performance. There was no significant difference in performance between a modular 2-class model and a single 3-class model system. The best models trained achieved a mean accuracy of 73% in classifying glioblastoma from other grades and 53% between WHO grade II and III gliomas. A visualization method was developed to convey the model output in a clinically relevant manner by overlaying color-coded predictions over the original whole-slide image. Conclusions: Our developed visualization method reflects the clinical decision-making process by highlighting the intratumor heterogeneity and may be used in a clinical setting to aid diagnosis. Explainable artificial intelligence techniques may allow further evaluation of the model and underline areas for improvements such as biases. Due to intratumor heterogeneity, data annotation for training was imprecise, and hence performance was lower than expected. The models may be further improved by employing advanced data augmentation strategies and using more precise semiautomatic or manually labeled training data

Direct jet coaxial electrospinning of axon-mimicking fibers for diffusion tensor imaging

Hollow polymer microfibers with variable microstructural and hydrophilic properties were proposed as building elements to create axon-mimicking phantoms for validation of diffusion tensor imaging (DTI). The axon-mimicking microfibers were fabricated in a mm-thick 3D anisotropic fiber strip, by direct jet coaxial electrospinning of PCL/polysiloxane-based surfactant (PSi) mixture as shell and polyethylene oxide (PEO) as core. Hydrophilic PCL-PSi fiber strips were first obtained by carefully selecting appropriate solvents for the core and appropriate fiber collector rotating and transverse speeds. The porous cross-section and anisotropic orientation of axon-mimicking fibers were then quantitatively evaluated using two ImageJ plugins—nearest distance (ND) and directionality based on their scanning electron microscopy (SEM) images. Third, axon-mimicking phantom was constructed from PCL-PSi fiber strips with variable porous-section and fiber orientation and tested on a 3T clinical MR scanner. The relationship between DTI measurements (mean diffusivity [MD] and fractional anisotropy [FA]) of phantom samples and their pore size and fiber orientation was investigated. Two key microstructural parameters of axon-mimicking phantoms including normalized pore distance and dispersion of fiber orientation could well interpret the variations in DTI measurements. Two PCL-PSi phantom samples made from different regions of the same fiber strips were found to have similar MD and FA values, indicating that the direct jet coaxial electrospun fiber strips had consistent microstructure. More importantly, the MD and FA values of the developed axon-mimicking phantoms were mostly in the biologically relevant range

Temperature dependence, accuracy, and repeatability of T-1 and T-2 relaxation times for the ISMRM/NIST system phantom measured using MR fingerprinting

Purpose Before MR fingerprinting (MRF) can be adopted clinically, the derived quantitative values must be proven accurate and repeatable over a range of T1 and T2 values and temperatures. Correct assessment of accuracy and precision as well as comparison between measurements can only be performed when temperature is either controlled or corrected for. The purpose of this study was to investigate the temperature dependence of T1 and T2 MRF values and evaluate the accuracy and repeatability of temperature-corrected relaxation values derived from a B1-corrected MRF–fast imaging with steady-state precession implementation using 2 different dictionary sizes. Methods The International Society of MR in Medicine/National Institute of Standards and Technology phantom was scanned using an MRF sequence of 2 different lengths, a variable flip angle T1, and a multi-echo spin echo T2 at 14 temperatures ranging from 15°C to 28°C and investigated with a linear regression model. Temperature-corrected accuracy was evaluated by correlating T1 and T2 times from each MRF dictionary with reference values. Repeatability was assessed using the coefficient of variation, with measurements taken over 30 separate sessions. Results There was a statistically significant fit of the model for MRF-derived T1 and T2 and temperature (p 500 ms. Both MRF methods showed a strong linear correlation with reference values for T1 (R2 = 0.996) and T2 (R2 = 0.982). MRF repeatability for T1 values was ≤1.4% and for T2 values was ≤3.4%. Conclusion MRF demonstrated relaxation times with a temperature dependence similar to that of conventional mapping methods. Temperature-corrected T1 and T2 values from both dictionaries showed adequate accuracy and excellent repeatability in this phantom study

An MRS- and PET-guided biopsy tool for intraoperative neuronavigational systems

OBJECTIVEGlioma heterogeneity and the limitations of conventional structural MRI for identifying aggressive tumor components can limit the reliability of stereotactic biopsy and, hence, tumor characterization, which is a hurdle for developing and selecting effective treatment strategies. In vivo MR spectroscopy (MRS) and PET enable noninvasive imaging of cellular metabolism relevant to proliferation and can detect regions of more highly active tumor. Here, the authors integrated presurgical PET and MRS with intraoperative neuronavigation to guide surgical biopsy and tumor sampling of brain gliomas with the aim of improving intraoperative tumor-tissue characterization and imaging biomarker validation.METHODSA novel intraoperative neuronavigation tool was developed as part of a study that aimed to sample high-choline tumor components identified by multivoxel MRS and 18F-methylcholine PET-CT. Spatially coregistered PET and MRS data were integrated into structural data sets and loaded onto an intraoperative neuronavigation system. High and low choline uptake/metabolite regions were represented as color-coded hollow spheres for targeted stereotactic biopsy and tumor sampling.RESULTSThe neurosurgeons found the 3D spherical targets readily identifiable on the interactive neuronavigation system. In one case, areas of high mitotic activity were identified on the basis of high 18F-methylcholine uptake and elevated choline ratios found with MRS in an otherwise low-grade tumor, which revealed the possible use of this technique for tumor characterization.CONCLUSIONSThese PET and MRI data can be combined and represented usefully for the surgeon in neuronavigation systems. This method enables neurosurgeons to sample tumor regions based on physiological and molecular imaging markers. The technique was applied for characterizing choline metabolism using MRS and 18F PET; however, this approach provides proof of principle for using different radionuclide tracers and other MRI methods, such as MR perfusion and diffusion.</jats:sec

Challenges for the functional diffusion map in pediatric brain tumors.

BackgroundThe functional diffusion map (fDM) has been suggested as a tool for early detection of tumor treatment efficacy. We aim to study 3 factors that could act as potential confounders in the fDM: areas of necrosis, tumor grade, and change in tumor size.MethodsThirty-four pediatric patients with brain tumors were enrolled in a retrospective study, approved by the local ethics committee, to examine the fDM. Tumors were selected to encompass a range of types and grades. A qualitative analysis was carried out to compare how fDM findings may be affected by each of the 3 confounders by comparing fDM findings to clinical image reports.ResultsResults show that the fDM in areas of necrosis do not discriminate between treatment response and tumor progression. Furthermore, tumor grade alters the behavior of the fDM: a decrease in apparent diffusion coefficient (ADC) is a sign of tumor progression in high-grade tumors and treatment response in low-grade tumors. Our results also suggest using only tumor area overlap between the 2 time points analyzed for the fDM in tumors of varying size.ConclusionsInterpretation of fDM results needs to take into account the underlying biology of both tumor and healthy tissue. Careful interpretation of the results is required with due consideration to areas of necrosis, tumor grade, and change in tumor size

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment