Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort.

OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

Optimal symptom combinations to aid COVID-19 case identification: analysis from a community-based, prospective, observational cohort.

OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

Probabilistic non-linear registration with spatially adaptive regularisation.

This paper introduces a novel method for inferring spatially varying regularisation in non-linear registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on the transformation parameters is parameterised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a traditional globally defined regularisation penalty, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The strength of the prior may be reduced in areas where the data better support deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce unwanted impacts of regularisation on the inferred transformation. This is especially important for applications where the deformation field itself is of interest, such as tensor based morphometry. The proposed approach is demonstrated using synthetic images, and with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results indicate that using the proposed spatially adaptive prior leads to sparser deformations, which provide better localisation of regional volume change. Additionally, the proposed regularisation model leads to more data driven and localised maps of registration uncertainty. This paper also demonstrates for the first time the use of Bayesian model comparison for selecting different types of regularisation.

In vivo imaging of tau pathology using multi-parametric quantitative MRI.

As the number of people diagnosed with Alzheimer's disease (AD) reaches epidemic proportions, there is an urgent need to develop effective treatment strategies to tackle the social and economic costs of this fatal condition. Dozens of candidate therapeutics are currently being tested in clinical trials, and compounds targeting the aberrant accumulation of tau proteins into neurofibrillary tangles (NFTs) are the focus of substantial current interest. Reliable, translatable biomarkers sensitive to both tau pathology and its modulation by treatment along with animal models that faithfully reflect aspects of the human disease are urgently required. Magnetic resonance imaging (MRI) is well established as a valuable tool for monitoring the structural brain changes that accompany AD progression. However the descent into dementia is not defined by macroscopic brain matter loss alone: non-invasive imaging measurements sensitive to protein accumulation, white matter integrity and cerebral haemodynamics probe distinct aspects of AD pathophysiology and may serve as superior biomarkers for assessing drug efficacy. Here we employ a multi-parametric array of five translatable MRI techniques to characterise the in vivo pathophysiological phenotype of the rTg4510 mouse model of tauopathy (structural imaging, diffusion tensor imaging (DTI), arterial spin labelling (ASL), chemical exchange saturation transfer (CEST) and glucose CEST). Tau-induced pathological changes included grey matter atrophy, increased radial diffusivity in the white matter, decreased amide proton transfer and hyperperfusion. We demonstrate that the above markers unambiguously discriminate between the transgenic group and age-matched controls and provide a comprehensive profile of the multifaceted neuropathological processes underlying the rTg4510 model. Furthermore, we show that ASL and DTI techniques offer heightened sensitivity to processes believed to precede detectable structural changes and, as such, provides a platform for the study of disease mechanisms and therapeutic intervention.

WE-E-213CD-03: Inverse-Consistent Symmetric Registration of Inner Colon Surfaces Derived from Prone and Supine CT Colonography.

PURPOSE: Robust registration of prone and supine colonie surfaces acquired during CT colonography may lead to faster and more accurate detection of colorectal cancer and polyps. Any directional bias when registering one surface to the other could precipitate incorrect anatomical correspondence and engender reader error. Despite this, non-rigid registration methods are often implemented asymmetrically, which could negatively influence the registration. We aimed to reduce directional bias and so increase robustness by adapting a cylindrical registration algorithm to be both symmetric and inverse-consistent. METHODS: The registration task can be simplified by mapping both prone and supine colonie surfaces onto regular cylinders. Spatial correspondence can then be established in cylindrical space using the original surfaces' local shape indices. We implemented a symmetric formulation of the popular non-rigid B-spline image registration method in cylindrical space. A symmetric similarity measure computes the sum of squared differences between both cylindrical representations of prone-to-supine and supine-to-prone directions simultaneously. Inverse consistency of the transformation is enforced by adding an appropriately weighted penalty term to the optimisation function. RESULTS: We selected 8 CT colonography patient cases with marked variation in luminal distension and surface morphology. We randomly allocated 4 of these for tuning an optimal set of registration parameters and 4 for validation. The mean inverse-consistency error was reduced by 32% from 4.8mm to 3.2mm by the new symmetric formulation. The mean registration error improved from 8.2mm to 7.3mm for 330 manually chosen reference points on the 4 validation sets. CONCLUSIONS: A symmetric formulation of prone and supine surface registration improves the quality of registration. Information from both prone-to-supine and supine-to-prone directions helps enforce convergence towards a more accurate solution due to reduced directional bias. A more robust and accurate registration will facilitate interpretation of CT colonography and has the potential to improve existing computer-aided detection methods. The authors gratefully acknowledge financial support for this work from the NIHR program: â€Å“Imaging diagnosis of colorectal cancer: Interventions for efficient and acceptable diagnosis in symptomatic and screening populationsâ€.

The effect of motion correction on pharmacokinetic parameter estimation in dynamic-contrast-enhanced MRI.

A dynamic-contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset consists of many imaging frames, often acquired both before and after contrast injection. Due to the length of time spent acquiring images, patient motion is likely and image re-alignment or registration is required before further analysis such as pharmacokinetic model fitting. Non-rigid image registration procedures may be used to correct motion artefacts; however, a careful choice of registration strategy is required to reduce misregistration artefacts associated with enhancing features. This work investigates the effect of registration on the results of model-fitting algorithms for 52 DCE-MR mammography cases for 14 patients. Results are divided into two sections: a comparison of registration strategies in which a DCE-MRI-specific algorithm is preferred in 50% of cases, followed by an investigation of parameter changes with known applied deformations, inspecting the effect of magnitude and timing of motion artefacts. Increased motion magnitude correlates with increased model-fit residual and is seen to have a strong influence on the visibility of strongly enhancing features. Motion artefacts in images close to the contrast agent arrival have a disproportionate effect on discrepancies in parameter estimation. The choice of algorithm, magnitude of motion and timing of the motion are each shown to influence estimated pharmacokinetic parameters even when motion magnitude is small.

Inter-fraction variations in respiratory motion models.

Respiratory motion can vary dramatically between the planning stage and the different fractions of radiotherapy treatment. Motion predictions used when constructing the radiotherapy plan may be unsuitable for later fractions of treatment. This paper presents a methodology for constructing patient-specific respiratory motion models and uses these models to evaluate and analyse the inter-fraction variations in the respiratory motion. The internal respiratory motion is determined from the deformable registration of Cine CT data and related to a respiratory surrogate signal derived from 3D skin surface data. Three different models for relating the internal motion to the surrogate signal have been investigated in this work. Data were acquired from six lung cancer patients. Two full datasets were acquired for each patient, one before the course of radiotherapy treatment and one at the end (approximately 6 weeks later). Separate models were built for each dataset. All models could accurately predict the respiratory motion in the same dataset, but had large errors when predicting the motion in the other dataset. Analysis of the inter-fraction variations revealed that most variations were spatially varying base-line shifts, but changes to the anatomy and the motion trajectories were also observed.

Patterns of cortical thinning in the language variants of frontotemporal lobar degeneration.

BACKGROUND: Frontotemporal lobar degeneration (FTLD) is a clinically, genetically, and pathologically heterogeneous neurodegenerative disorder. Two subtypes commonly present with a language disorder: semantic dementia (SemD) and progressive nonfluent aphasia (PNFA). METHODS: Patients meeting consensus criteria for PNFA and SemD who had volumetric MRI of sufficient quality to allow cortical thickness analysis were recruited from a tertiary referral clinic: 44 (11 pathologically confirmed) patients with SemD and 32 (4 pathologically confirmed) patients with PNFA and 29 age-matched and gender-matched healthy controls were recruited. Cortical thickness analysis was performed using the Freesurfer software tools. RESULTS: Patients with SemD had significant cortical thinning in the left temporal lobe, particularly temporal pole, entorhinal cortex, and parahippocampal, fusiform, and inferior temporal gyri. A similar but less extensive pattern of loss was seen in the right temporal lobe and (with increasing severity) also in left orbitofrontal, inferior frontal, insular, and cingulate cortices. Patients with PNFA had involvement particularly of the left superior temporal lobe, inferior frontal lobe, and insula, and (with increasing severity) other areas in the left frontal, lateral temporal, and anterior parietal lobes. Similar patterns were seen in the pathologically confirmed cases. Patterns of cortical thinning differed between groups: SemD had significantly more cortical thinning in the temporal lobes bilaterally while PNFA had significantly more thinning in the frontal and parietal lobes. CONCLUSIONS: The language variants of frontotemporal lobar degeneration have distinctive and significantly different patterns of cortical thinning. Increasing disease severity is associated with spread of cortical thinning and the pattern of spread is consistent with progression of clinical deficits.