Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.

OBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD

Visual ratings of atrophy in MCI: prediction of conversion and relationship with CSF biomarkers.

Medial temporal lobe atrophy (MTA) and cerebrospinal fluid (CSF) markers of Alzheimer's disease (AD) pathology may aid the early detection of AD in mild cognitive impairment (MCI). However, the relationship between structural and pathological markers is not well understood. Furthermore, while posterior atrophy (PA) is well recognized in AD, its value in predicting conversion from late-onset amnestic MCI to AD is unclear. In this study we used visual ratings of MTA and PA to assess their value in predicting conversion to AD in 394 MCI patients. The relationship of atrophy patterns with CSF Aβ1-42, tau, and p-tau(181) was further investigated in 114 controls, 192 MCI, and 99 AD patients. There was a strong association of MTA ratings with conversion to AD (p < 0.001), with a weaker association for PA ratings (p = 0.047). Specific associations between visual ratings and CSF biomarkers were found; MTA was associated with lower levels of Aβ1-42 in MCI, while PA was associated with elevated levels of tau in MCI and AD, which may reflect widespread neuronal loss including posterior regions. These findings suggest both that posterior atrophy may predict conversion to AD in late-onset MCI, and that there may be differential relationships between CSF biomarkers and regional atrophy patterns

Multi-Atlas Segmentation of Biomedical Images: A Survey

Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing

Characterising population variability in brain structure through models of whole-brain structural connectivity

Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender

STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation

Anatomical segmentation of structures of interest is critical to quantitative analysis in medical imaging. Several automated multi-atlas based segmentation propagation methods that utilise manual delineations from multiple templates appear promising. However, high levels of accuracy and reliability are needed for use in diagnosis or in clinical trials. We propose a new local ranking strategy for template selection based on the locally normalised cross correlation (LNCC) and an extension to the classical STAPLE algorithm by Warfield et al. (2004), which we refer to as STEPS for Similarity and Truth Estimation for Propagated Segmentations. It addresses the well-known problems of local vs. global image matching and the bias introduced in the performance estimation due to structure size. We assessed the method on hippocampal segmentation using a leave-one-out cross validation with optimised model parameters; STEPS achieved a mean Dice score of 0.925 when compared with manual segmentation. This was significantly better in terms of segmentation accuracy when compared to other state-of-the-art fusion techniques. Furthermore, due to the finer anatomical scale, STEPS also obtains more accurate segmentations even when using only a third of the templates, reducing the dependence on large template databases. Using a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) scans from different MRI imaging systems and protocols, STEPS yielded similarly accurate segmentations (Dice=0.903). A cross-sectional and longitudinal hippocampal volumetric study was performed on the ADNI database. Mean±SD hippocampal volume (mm(3)) was 5195 ± 656 for controls; 4786 ± 781 for MCI; and 4427 ± 903 for Alzheimer's disease patients and hippocampal atrophy rates (%/year) of 1.09 ± 3.0, 2.74 ± 3.5 and 4.04 ± 3.6 respectively. Statistically significant (p<10(-3)) differences were found between disease groups for both hippocampal volume and volume change rates. Finally, STEPS was also applied in a multi-label segmentation propagation scenario using a leave-one-out cross validation, in order to parcellate 83 separate structures of the brain. Comparisons of STEPS with state-of-the-art multi-label fusion algorithms showed statistically significant segmentation accuracy improvements (p<10(-4)) in several key structures

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Cholinergic system in sequelae of traumatic brain injury

Background: Traumatic brain injury (TBI) is one of the most significant causes of disability and lowered capacity. TBI cause also a considerable financial burden since the majority of patients are young at the time of injury. Though much scientific work has been conducted, the pathophysiological mechanisms behind the sequelae of TBI are still largely unknown. However, there is evidence emerging from experimental and clinical studies that the cholinergic system seems to be at least partly involved in the cognitive impairment associated with TBI. In the TBI aftermath, patients commonly experience problems with attention, initiative and processing speed, i.e. functions which are mainly regulated by the cholinergic system. Additionally, in particular there are indications that the structures containing acetylcholinecontaining neurons are commonly injured in TBI. Furthermore, there is preliminary evidence that at least some TBI patients may benefit from cholinergic medication. Aims of the study: Our aim was to utilize positron emission tomography (PET) and magnetic resonance imaging (MRI) to evaluate possible alterations in the cholinergic system after TBI. An additional goal was to clarify the association of these structural or functional changes to the patient’s response to cholinergic medication. Patients with moderate-to-severe TBI were compared to healthy controls with PET using the [11C]MP4A tracer. MP4A targets acetylcholinesterase (AChE), which is the pre- and post-synaptic acetylcholine degrading enzyme. The TBI patient group was divided into two depending on their response to rivastigmine (inhibitor of AChE) treatment. These patient groups were imaged with MP4A-PET at baseline (without medication) and after 4 weeks of rivastigmine therapy to compare differences in AChE activity. Cholinergic structures were also investigated with atlas-based MRI morphometry. It was also examined whether the atrophy rates of frontal cholinergic structures were associated with neuropsychological tests results. The subjects filled in a questionnaire to determine whether their smoking histories had any connection to the outcome of TBI. Results: The AChE activity in TBI patients was clearly lowered in cortical regions when compared to controls. Most significantly, AChE activity was reduced in parieto- and occipital-cortices. A comparison of the two TBI patient groups in the primary time point scan showed evidence of lowered AChE activity in frontal cortical structures in rivastigmine responders. However, the inhibitory effect of rivastigmine on AChE activity was similar with patient groups when scanned during drug therapy and there was no longer any significant difference between groups in their AChE activities. MRI morphometry revealed that the higher the atrophy rate in frontal cortical structures, the poorer the performance in neuropsychological tests measuring attention. Smoking history was not associated with TBI outcome. Conclusions: According to the results of this study, it appears that the cholinergic system is altered chronically after TBI. It also seems that these structural alterations and the consequential functional changes in the cholinergic system are connected to the response to cholinergic medication. Additionally, the atrophy rate of frontal cortical structures, which are mainly innervated by cholinergic neurons, appears to have correlation to neuropsychological performance concerning attention. There did not seem to be any link between smoking and TBI outcome

Longitudinal neuroimaging measures of volumetric change across the frontotemporal dementia spectrum

Frontotemporal dementia (FTD) is a common cause of young onset dementia, encompassing several clinical, genetic and pathological subgroups. Currently there are no treatments, but there are promising candidates in development. However, proven biomarkers of disease progression in FTD are lacking and urgently needed to facilitate these trials. Investigating large sporadic and genetic FTD cohorts, this thesis provides a comprehensive comparison of longitudinal neuroimaging measures of structural change within the clinical, genetic and pathological FTD subgroups. Effect size and sample size estimates are computed to explore the feasibility of these brain measures as surrogate markers of disease progression in order to detect disease-modifying treatment effects. The first project compares 17 automated techniques for extracting whole-brain atrophy measures. Many of the techniques showed great promise, producing sample sizes of substantially less than 100 patients required to detect a disease-modifying effect. Significant differences in performance were found between both techniques and patient subgroups, highlighting the importance of informed biomarker choice in matching the optimal marker to the patient group to be enrolled in a trial. In the following chapters, I explored lobar and subcortical change across the disease spectrum. The different patient subgroups presented with unique profiles of change but, interestingly, automated measures of temporal lobe, caudate and thalamic atrophy proved to be particularly sensitive markers of change, producing low sample size estimates across the FTD subgroups. Importantly, I found significantly increased rates of amygdala, hippocampus, caudate and thalamic atrophy in differing patterns across presymptomatic mutation carriers, providing the first comprehensive assessment of the utility of such markers for early therapeutic intervention at this ideal stage before symptoms develop. In summary, this work expands current knowledge and builds on the limited longitudinal investigations currently available in FTD, as well as providing valuable information about the potential of non-invasive biomarkers for sporadic and genetic FTD trials