325 research outputs found
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data
Despite the remarkable advances in cancer diagnosis, treatment, and
management that have occurred over the past decade, malignant tumors remain a
major public health problem. Further progress in combating cancer may be
enabled by personalizing the delivery of therapies according to the predicted
response for each individual patient. The design of personalized therapies
requires patient-specific information integrated into an appropriate
mathematical model of tumor response. A fundamental barrier to realizing this
paradigm is the current lack of a rigorous, yet practical, mathematical theory
of tumor initiation, development, invasion, and response to therapy. In this
review, we begin by providing an overview of different approaches to modeling
tumor growth and treatment, including mechanistic as well as data-driven models
based on ``big data" and artificial intelligence. Next, we present illustrative
examples of mathematical models manifesting their utility and discussing the
limitations of stand-alone mechanistic and data-driven models. We further
discuss the potential of mechanistic models for not only predicting, but also
optimizing response to therapy on a patient-specific basis. We then discuss
current efforts and future possibilities to integrate mechanistic and
data-driven models. We conclude by proposing five fundamental challenges that
must be addressed to fully realize personalized care for cancer patients driven
by computational models
MRI quantification of blood-brain barrier leakage in the ageing brain
Cerebral small vessel disease, or SVD, refers to processes that lead to dysfunction and
damage in cerebral microvessels, and is implicated in ischaemic stroke and vascular
dementia. Although the pathophysiology is poorly understood, a subtle breakdown in the
blood-brain barrier (BBB) has been implicated as a potential underlying mechanism. BBB
breakdown is difficult to measure in-vivo however - Dynamic Contrast-Enhanced Magnetic
Resonance Imaging (DCE-MRI) is the dominant technique for assessing BBB integrity in
clinical populations and is the focus of the work presented in this thesis. In this work, there
are two main objectives:
1. Assess and optimise current DCE-MRI processing methods to provide accurate
measurements of BBB breakdown.
2. Relate BBB breakdown to other features of SVD: clinical outcomes, imaging markers, and
risk factors.
To achieve the first objective, simulations were used to estimate the effects of various
technical and modelling errors in measured BBB breakdown. By generating a realistic
simulation of biological processes during a DCE-MRI sequence, sources of systematic error
could be identified along with potential solutions. The implementation of MRI processing
recommendations (a slow injection of contrast agent, exclusion of first-pass data from
model fitting, and the use of a novel fitting method that better represents underlying
biophysics) was found to reduce the sensitivity of calculated DCE-MRI parameters to the
effects of variable blood plasma flow, variable water exchange rates, and injection delay by
over 90%. Additionally, correction for field inhomogeneities was also found to reduce the
error of calculated DCE-MRI parameters. Combining all the suggested processing methods
was found to reduce the systematic error of calculated DCE-MRI parameters by up to 97%.
These simulations form the basis of an open access framework and include an accessible
GUI (1).
For the second objective data was obtained from a prospective cohort study of mild stroke
patients, and multiple linear regression was used to investigate how regional BBB
breakdown is related to various patient factors. Regression models were controlled for
several potential confounds and were implemented for both cross-sectional and
longitudinal data. It was found that areas of hyperintensity on MRI images (which are
indicative of vascular damage) presented lesser BBB breakdown when the severity of
imaging markers was greater. Additionally, greater breakdown in the BBB of the basal
ganglia is associated with greater disability scores, suggesting that vascular damage in this
region may affect motor function and cognition. Risk factors associated with greater BBB
breakdown include: age, a diagnosis of hypertension, and a diagnosis of diabetes, although
the causality of these relationships is unclear.
In summary, this thesis aims to improve the measurement of subtle BBB breakdown using
DCE-MRI, and then use the optimised methods to investigate how BBB breakdown relates to
clinical outcomes, imaging markers, and risk factors associated with SVD
Measurement of treatment response and survival prediction in malignant pleural mesothelioma
Malignant pleural mesothelioma (MPM) is a rare cancer of the mesothelial cells of the visceral and parietal pleurae that is heterogeneous in terms of biology, prognosis and response to systemic anti-cancer therapy (SACT). The primary tumour forms an unusual, complex shape which makes survival prediction and response measurement uniquely challenging. Computed tomography (CT) imaging is the bedrock of radiological quantification and response assessment, but it has major limitations that translate into low sensitivity and high inter-observer variation when classifying response using Response Evaluation Classification In Solid Tumours (mRECIST) criteria. Magnetic resonance imaging (MRI) tools have been developed that overcome some of these problems but cost and availability of MRI mean that optimisation of CT and better use for data acquired by this method are important priorities in the short term. In this thesis, I conducted 3 studies focused on, 1) development of a semi-automated volumetric segmentation method for CT based on recently positive studies in MRI, 2) training and external validation of a deep learning artificial intelligence (AI) tool for fully automated volumetric segmentation based on CT data, and, 3) use of non-tumour imaging features available from CT related to altered body composition for development of new prognostic models, which could assist in selection of patients for treatment and improving tolerance to treatment by targeting the systemic consequences of MPM.
The aim of Chapter 3 is to develop a semi-automated MPM tumour volume segmentation method that would serve as the ground truth for the training of a fully automated AI algorithm. A semi-automated approach to pleural tumour segmentation has been developed using MRI scans which calculated volumetric measurements from seed points - defined by differential tumour enhancement - placed within a pre-defined volume of pleural tumour. I extrapolated this MRI method using contrast-enhanced CT scans in 23 patients with MPM. Radiodensity values – defined by Hounsfield units (HU) - were calculated for the different thoracic tissues by placing regions of interest (ROI) on visible areas of pleural tumour with similar ROIs placed on other thoracic tissues. Pleural volume contours were drawn on axial CT slices and propagated throughout the volume by linear interpolation using volumetric software (Myrian Intrasense® software v2.4.3 (Paris, France)). Seed points based on the radiodensity range of pleural tumour were placed on representative areas of tumour with regions grown. There were similarities in median thoracic tissue HU values: pleural tumour, 52 [IQR 46 to 60] HU; intercostal muscle, 20.4 [IQR 11.9 to 32.3] HU; diaphragm, 40.4 [IQR 26.4 to 56.4] HU and pleural fluid, 11.8 [IQR 8.3 to 17.8] HU. There was also reduced definition between MPM tumour and neighbouring structures. The mean time taken to complete semi-automated volumetric segmentations for the 8 CT scans examined was 25 (SD 7) minutes. The semi-automated CT volumes were larger than the MRI volumes with a mean difference between MRI and CT volumes of -457.6 cm3 (95% limits of agreement -2741 to +1826 cm3). The complex shape of MPM tumour and overlapping thoracic tissue HU values precluded HU threshold-based region growing and meant that semi-automated volumetry using CT was not possible in this thesis.
Chapter 4 describes a multicentre retrospective cohort study that developed and validated an automated AI algorithm – termed a deep learning Convolutional Neural Network (CNN) - for volumetric MPM tumour segmentation. Due to the limitations of the semi-automated approach described in Chapter 3, manually annotated tumour volumes were used to train the CNN. The manual segmentation method ensured that all the parietal pleural tumour was included in the respective volumes. Although the manual CT volumes were consistently smaller than semi-automated MRI volumes (average difference between AI and human volumes 74.8 cm3), they were moderately correlated (Pearson’s r=0.524, p=0.0103). There was strong correlation (external validation set r=0.851, p<0.0001) and agreement (external validation set mean AI minus human volume difference of +31 cm3 between human and AI tumour volumes). AI segmentation errors (4/60 external validation set cases) were associated with complex anatomical features. There was agreement between human and AI volumetric responses in 20/30 (67%) cases. There was agreement between AI volumetric and mRECIST classification responses in 16/30 (55%) cases. Overall survival (OS) was shorter in patients with higher AI-defined pre-chemotherapy tumour volumes (HR=2.40, 95% CI 1.07 to 5.41, p=0.0114).
Survival prediction in MPM is difficult due to the heterogeneity of the disease. Previous survival prediction models have not included measures of body composition which are prognostic in other solid organ cancers. In Chapter 5, I explore the impact of loss of skeletal muscle and adipose tissue at the level of the third lumbar vertebra (L3) and the loss of skeletal muscle at the fourth thoracic (T4) vertebrae on survival and response to treatment in patients with MPM receiving chemotherapy. Skeletal and adipose muscle areas at L3 and T4 were quantified by manual delineation of relevant muscle and fat groups using ImageJ software (U.S. National Institutes of Health, Bethesda, MD) on pre-chemotherapy and response assessment CT scans, with normalisation for height. Sarcopenia at L3 was not associated with shorter OS at the pre-chemotherapy (HR 1.49, 95% CI 0.95 to 2.52, p=0.077) or response assessment time points (HR 1.48, 95% CI 0.97 to 2.26, p=0.0536). A higher visceral adipose tissue index (VFI) measured at L3 was associated with shorter OS (HR 1.95, 95% CI 1.05 to 3.62, p=0.0067). In multivariate analysis, obesity was associated with improved OS (HR 0.36, 95% CI 0.20 to 0.65, p<0.001) while interval VFI loss (HR 1.81, 95% CI 1.04 to 3.13, p=0.035) was associated with reduced OS. Overall loss of skeletal muscle index at the fourth thoracic vertebra (T4SMI) during treatment was associated with poorer OS (HR 2.79, 95% CI 1.22 to 6.40, p<0.0001). Skeletal muscle index on the ipsilateral side of the tumour at the fourth thoracic vertebra (Ipsilateral T4SMI) loss was also associated with shorter OS (HR 2.91, 95% CI 1.28 to 6.59, p<0.0001). In separate multivariate models, overall T4SMI muscle loss (HR 2.15, 95% CI 102 to 4.54, p=0.045) and ipsilateral T4SMI muscle loss (HR 2.85, 95% CI 1.17 to 6.94, p=0.021) were independent predictors of OS. Response to chemotherapy was not associated with decreasing skeletal muscle or adipose tissue indices
Open‐source magnetic resonance imaging: improving access, science, and education through global collaboration
Open-source practices and resources in magnetic resonance imaging (MRI) have increased substantially in recent years. This trend started with software and data being published open-source and, more recently, open-source hardware designs have become increasingly available. These developments towards a culture of sharing and establishing nonexclusive global collaborations have already improved the reproducibility and reusability of code and designs, while providing a more inclusive approach, especially for low-income settings. Community-driven standardization and documentation efforts are further strengthening and expanding these milestones. The future of open-source MRI is bright and we have just started to discover its full collaborative potential. In this review we will give an overview of open-source software and open-source hardware projects in human MRI research
Nuclear Imaging and Therapy:Towards a Personalized Approach in HCC and NET
This thesis explores new applications of nuclear imaging and therapy in patients with hepatocellular carcinoma (HCC) and neuroendocrine tumors (NET). These diseases are often detected late, making curative therapy not always possible. Developments in positron emission tomography (PET) and radionuclide therapy have led to new nuclear agents. The aim of this thesis is to provide insight into several new applications of current and new tracers in the diagnosis and treatment of HCC and NET.One of the investigated tracers is 18F-DOPA, which is currently used for NET tumors that are negative on 68Ga-labeled somatostatin analog (SSA) PET scans. Our study confirms the equivalent detection of 18F-DOPA in tumor detection compared to 68Ga-SSAs. Selective internal radiation therapy (SIRT) uses yttrium-90 radioactive resin spheres that are intravascularly injected into the liver. Higher than usual dosages (>120 Gy) appear to lead to better results in tumor reduction and the effects not only seem to be greater but also longer lasting.Furthermore, we demonstrated that 11C-Choline and 18F-FDG together find more tumors that are relevant for clinical decision-making in patients suspected of HCC recurrence. The thesis also offers two prospective study protocols, namely a comparison of 68Ga-DOTA-TOC with the new somatostatin tracer 18F-SiTATE in NET and a comparison of ablation with SIRT as a bridge strategy in liver transplantation. These results suggest that broader use of 18F-DOPA in PET diagnosis of NET is possible and that higher tumor-targeted dosages in SIRT can lead to better treatment
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