16 results found
Truong AH, Sharmanska V, Limback-Stanic C, et al., 2020, Optimisation of deep learning methods for visualisation of tumour heterogeneity and brain tumour grading through digital pathology, Neuro-Oncology Advances, Vol: 2, ISSN: 2632-2498
BackgroundVariations in prognosis and treatment options for gliomas are dependent on tumour 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 tumour heterogeneity, sampling error and subjectivity, and hence there is great inter-observer variability in readings.MethodsWe trained convolutional neural network models to classify digital whole-slide histopathology images from The Cancer Genome Atlas. We tested a number of optimisation parameters.ResultsData augmentation did not improve model training, while 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 visualisation method was developed to convey the model output in a clinically relevant manner by overlaying colour-coded predictions over the original whole slide image.ConclusionsOur developed visualisation method reflects the clinical decision-making process by highlighting the intra-tumour heterogeneity and may be used in clinical setting to aid diagnosis. Explainable AI techniques may allow further evaluation of the model and underline areas for improvements such as biases. Due to intra-tumour 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 semi-automatic or manually labelled training data.
Grech-Sollars M, Ordidge KL, Vaqas B, et al., 2019, Imaging and tissue biomarkers of choline metabolism in diffuse adult glioma; 18F-fluoromethylcholine PET/CT, magnetic resonance spectroscopy, and choline kinase α, Cancers, Vol: 11, Pages: 1-15, ISSN: 2072-6694
The cellular and molecular basis of choline uptake on PET imaging and MRS-visible choline containing compounds is not well understood. Choline kinase alpha (ChoKa) is an enzyme that phosphorylates choline, an essential step in membrane synthesis. We investigate choline metabolism through 18F-fluoromethylcholine (18F-FMC) PET, MRS and tissue ChoKa in human glioma. 14 patients with suspected diffuse glioma underwent multimodal 3T MRI and dynamic 18FFMC PET/CT prior to surgery. Co-registered PET and MRI data were used to target biopsies to regions of high and low choline signal, and immunohistochemistry for ChoKa expression was performed. 18F-FMC/PET differentiated WHO grade IV from grade II and III tumours, whereas MRS differentiated grade III/IV from grade II tumours. Tumoural 18F-FMC/PET uptake was higher than in normal-appearing white matter across all grades and markedly elevated within regions of contrast enhancement. 18F-FMC/PET correlated weakly with MRS Cho ratios. ChoKa expression on IHC was negative or weak in all but one GBM sample, and did not correlate with tumour grade or imaging choline markers. MRS and 18F-FMC/PET provide complimentary information on glioma choline metabolism. Tracer uptake is, however, potentially confounded by blood-brain barrier permeability. ChoKa overexpression does not appear to be a common feature in diffuse glioma.
Inglese M, Katherine L O, Lesley H, et al., 2019, Reliability of dynamic contrast enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models, Neuroradiology, Vol: 61, Pages: 1375-1386, ISSN: 0028-3940
PurposeTo investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumourdata and to ascertain reliable perfusion parameters through a model selection processand a stability test.MethodsDCE-MRI data of 14 patients with primary brain tumours were analysed using the Toftsmodel (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and theextended shutter speed model (ESSM). A no-effect model (NEM) was implemented toassess overfitting of data by the other models.For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D modelselection map. The variability of each pharmacokinetic parameter extracted from thismap was assessed with a noise propagation procedure, resulting in voxel-wisedistributions of the coefficient of variation (CV).ResultsThe model selection map over all patients showed NEM had the best fit in 35.5% ofvoxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (<0.1%). Inanalysing the reliability of Ktrans, when considering regions with a CV<20%, ≈25% ofvoxels were found to be stable across all patients. The remaining 75% of voxels wereconsidered unreliable.ConclusionsThe majority of studies quantifying DCE-MRI data in brain tumours only consider asingle model and whole-tumour statistics for the output parameters. Appropriate modelselection, considering tissue biology and its effects on blood brain barrier permeabilityand exchange conditions, together with an analysis on the reliability and stability of thecalculated parameters, is critical in processing robust brain tumour DCE-MRI data.
Bangerter NK, Morrell G, Grech-Sollars M, 2019, Magnetic resonance imaging, Bioengineering Innovative Solutions for Cancer, Pages: 163-194, ISBN: 9780128138878
Magnetic resonance imaging (MRI) is a generally noninvasive imaging modality that is highly flexible and configurable and can achieve excellent contrast between soft tissues in the body. Since its invention and initial development in the 1970s, the number of MRI techniques available in the laboratory and the clinic has rapidly expanded. Acquisition parameters can now be customized to generate not only two- and three-dimensional images of anatomical structures in the body but also images showing metabolic activity, blood flow velocities, and even the diffusion characteristics of water molecules in tissue. Application of MRI techniques to the study of cancer is widespread, from detection, diagnosis, and characterization of disease, to tumor response to therapy. This chapter provides background on the fundamental concepts and physics that make magnetic resonance imaging possible and then builds on this framework to provide a description of the most common uses of MRI for cancer in both the clinic and the laboratory.
Grech-Sollars M, Zhou F-L, Waldman AD, et al., 2018, Stability and reproducibility of co-electrospun brain-mimicking phantoms for quality assurance of diffusion MRI sequences, NeuroImage, Vol: 181, Pages: 395-402, ISSN: 1053-8119
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.
Morrison M, Islam S, Waldman A, et al., 2018, A HISTOGRAM-BASED, BACK-PROJECTION METHOD FOR TREATMENT RESPONSE ASSESSMENT IN GLIOBLASTOMA USING MULTI B-VALUE ADVANCED DIFFUSION MRI, 23rd Annual Scientific Meeting and Education Day of the Society-for-Neuro-Oncology (SNO) / 3rd CNS Anticancer Drug Discovery and Development Conference, Publisher: OXFORD UNIV PRESS INC, Pages: 186-187, ISSN: 1522-8517
Grech-Sollars M, Inglese M, Ordidge K, et al., 2018, ASSOCIATION BETWEEN METABOLIC PARAMETERS FROM DYNAMIC 18FMC PET, PHARMACOKINETIC DCE-MRI PARAMETERS, MRS CHOLINE TO CREATINE RATIOS AND TISSUE IMMUNOHISTOCHEMISTRY FOR CHOLINE KINASE ALPHA EXPRESSION IN HUMAN BRAIN GLIOMA, 23rd Annual Scientific Meeting and Education Day of the Society-for-Neuro-Oncology (SNO) / 3rd CNS Anticancer Drug Discovery and Development Conference, Publisher: OXFORD UNIV PRESS INC, Pages: 184-184, ISSN: 1522-8517
Islam S, Morrison M, Grech-Sollars M, et al., 2018, THE USE OF ADVANCED DIFFUSION MRI PARAMETERS IN THE ASSESSMENT OF TREATMENT RESPONSE IN GLIOBLASTOMA USING MULTI-B VALUE ACQUISITION AND A HISTOGRAM-BASED APPROACH, 23rd Annual Scientific Meeting and Education Day of the Society-for-Neuro-Oncology (SNO) / 3rd CNS Anticancer Drug Discovery and Development Conference, Publisher: OXFORD UNIV PRESS INC, Pages: 20-21, ISSN: 1522-8517
Grech-Sollars M, Inglese M, Ordidge K, et al., 2018, ASSOCIATION BETWEEN METABOLIC PARAMETERS FROM DYNAMIC 18F-FLUOROMETHYLCHOLINE PET, PHARMACOKINETIC PARAMETERS FROM DCE-MRI, CHOLINE TO CREATINE RATIOS FROM MRS AND TISSUE IMMUNOHISTOCHEMISTRY FOR CHOLINE KINASE ALPHA EXPRESSION IN HUMAN BRAIN GLIOMA, Meeting of the British-Neuro-Oncology-Society (BNOS), Publisher: OXFORD UNIV PRESS INC, Pages: 346-346, ISSN: 1522-8517
Inglese M, Honeyfield L, Aboagye E, et al., 2018, Comparison of the Tofts and the Shutter Speed Model for DCE-MRI in patients with Brain Glioma, 27th International Society for Magnetic Resonance in Medicine
Grech-Sollars M, Vaqas B, Thompson G, et al., 2017, An MRS- and PET-guided biopsy tool for intraoperative neuronavigational systems., J Neurosurg, Vol: 127, Pages: 812-818
OBJECTIVE Glioma 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. METHODS A 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. RESULTS The 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. CONCLUSIONS These 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 m
King MD, Grech-Sollars M, 2016, A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo (MCMC) simulation, F1000 Research, Vol: 5, ISSN: 2046-1402
The focus of this study is the development of a statistical modelling procedure for characterisingintra-tumour heterogeneity, motivated by recent clinical literature indicating that a varietyof tumours exhibit a considerable degree of genetic spatial variability. A formal spatial statisticalmodel has been developed and used to characterise the structural heterogeneity of anumber of supratentorial primitive neuroecto-dermal tumours (PNETs), based on diffusionweightedmagnetic resonance imaging. Particular attention is paid to the spatial dependenceof diffusion close to the tumour boundary, in order to determine whether the data providestatistical evidence to support the proposition that water diffusivity in the boundary region ofsome tumours exhibits a deterministic dependence on distance from the boundary, in excessof an underlying random 2D spatial heterogeneity in diffusion. Tumour spatial heterogeneitymeasures were derived from the diffusion parameter estimates obtained using a Bayesianspatial random effects model. The analyses were implemented using Markov chain MonteCarlo (MCMC) simulation. Posterior predictive simulation was used to assess the adequacyof the statistical model. The main observations are that the previously reported relationshipbetween diffusion and boundary proximity remains observable and achieves statistical significanceafter adjusting for an underlying random 2D spatial heterogeneity in the diffusionmodel parameters. A comparison of the magnitude of the boundary-distance effect with theunderlying random 2D boundary heterogeneity suggests that both are important sources ofvariation in the vicinity of the boundary. No consistent pattern emerges from a comparison ofthe boundary and core spatial heterogeneity, with no indication of a consistently greater levelof heterogeneity in one region compared with the other. The results raise the possibility thatDWI might provide a surrogate marker of intra-tumour genetic regional heterogeneity, whichwould
Grech-Sollars M, Hales PW, Miyazaki K, et al., 2015, Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain., NMR in Biomedicine, Vol: 28, Pages: 468-485, ISSN: 0952-3480
The purpose of this work was to assess the reproducibility of diffusion imaging, and in particular the apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM) parameters and diffusion tensor imaging (DTI) parameters, across multiple centres using clinically available protocols with limited harmonization between sequences. An ice-water phantom and nine healthy volunteers were scanned across fives centres on eight scanners (four Siemens 1.5T, four Philips 3T). The mean ADC, IVIM parameters (diffusion coefficient D and perfusion fraction f) and DTI parameters (mean diffusivity MD and fractional anisotropy FA), were measured in grey matter, white matter and specific brain sub-regions. A mixed effect model was used to measure the intra- and inter-scanner coefficient of variation (CV) for each of the five parameters. ADC, D, MD and FA had a good intra- and inter-scanner reproducibility in both grey and white matter, with a CV ranging between 1% and 7.4%; mean 2.6%. Other brain regions also showed high levels of reproducibility except for small structures such as the choroid plexus. The IVIM parameter f had a higher intra-scanner CV of 8.4% and inter-scanner CV of 24.8%. No major difference in the inter-scanner CV for ADC, D, MD and FA was observed when analysing the 1.5T and 3T scanners separately. ADC, D, MD and FA all showed good intra-scanner reproducibility, with the inter-scanner reproducibility being comparable or faring slightly worse, suggesting that using data from multiple scanners does not have an adverse effect compared with using data from the same scanner. The IVIM parameter f had a poorer inter-scanner CV when scanners of different field strengths were combined, and the parameter was also affected by the scan acquisition resolution. This study shows that the majority of diffusion MRI derived parameters are robust across 1.5T and 3T scanners and suitable for use in multi-centre clinical studies and trials.
Grech-Sollars M, Saunders DE, Phipps KP, et al., 2014, Challenges for the functional diffusion map in pediatric brain tumors, NEURO-ONCOLOGY, Vol: 16, Pages: 449-456, ISSN: 1522-8517
Grech-Sollars M, Saunders DE, Phipps KP, et al., 2013, Response to "Reply to 'Survival analysis for apparent diffusion coefficient measures in children with embryonal brain tumors,' by Grech-Sollars et al", NEURO-ONCOLOGY, Vol: 15, Pages: 268-268, ISSN: 1522-8517
Grech-Sollars M, Saunders DE, Phipps KP, et al., 2012, Survival analysis for apparent diffusion coefficient measures in children with embryonal brain tumours, NEURO-ONCOLOGY, Vol: 14, Pages: 1285-1293, ISSN: 1522-8517
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