Imperial College London

DrTimothyDawes

Faculty of MedicineNational Heart & Lung Institute

Honorary Clinical Lecturer
 
 
 
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Contact

 

+44 (0)20 3313 3298tim.dawes Website

 
 
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Location

 

MRC London Institute of Medical Sciences, 111 ISDRobert Steiner MR unitHammersmith Campus

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Summary

 

Publications

Publication Type
Year
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74 results found

Dawes TJW, McCabe C, Dimopoulos K, Stewart I, Bax S, Harries C, Samaranayake CB, Kempny A, Molyneaux PL, Seitler S, Semple T, Li W, George PM, Kouranos V, Chua F, Renzoni EA, Kokosi M, Jenkins G, Wells AU, Wort SJ, Price LCet al., 2023, Phosphodiesterase 5 inhibitor treatment and survival in interstitial lung disease pulmonary hypertension: A Bayesian retrospective observational cohort study, Respirology, Vol: 28, Pages: 262-272, ISSN: 1323-7799

Background and ObjectivePulmonary hypertension is a life-limiting complication of interstitial lung disease (ILD-PH). We investigated whether treatment with phosphodiesterase 5 inhibitors (PDE5i) in patients with ILD-PH was associated with improved survival.MethodsConsecutive incident patients with ILD-PH and right heart catheterisation, echocardiography and spirometry data were followed from diagnosis to death, transplantation or censoring with all follow-up and survival data modelled by Bayesian methods.ResultsThe diagnoses in 128 patients were idiopathic pulmonary fibrosis (n = 74, 58%), hypersensitivity pneumonitis (n = 17, 13%), non-specific interstitial pneumonia (n = 12, 9%), undifferentiated ILD (n = 8, 6%) and other lung diseases (n = 17, 13%). Final outcomes were death (n = 106, 83%), transplantation (n = 9, 7%) and censoring (n = 13, 10%). Patients treated with PDE5i (n = 50, 39%) had higher mean pulmonary artery pressure (median 38 mm Hg [interquartile range, IQR: 34, 43] vs. 35 mm Hg [IQR: 31, 38], p = 0.07) and percentage predicted forced vital capacity (FVC; median 57% [IQR: 51, 73] vs. 52% [IQR: 45, 66], p=0.08) though differences did not reach significance. Patients treated with PDE5i survived longer than untreated patients (median 2.18 years [95% CI: 1.43, 3.04] vs. 0.94 years [0.69, 1.51], p = 0.003) independent of all other prognostic markers by Bayesian joint-modelling (HR 0.39, 95% CI: 0.23, 0.59, p < 0.001) and propensity-matched analyses (HR 0.38, 95% CI: 0.22, 0.58, p < 0.001). Survival difference with treatment was significantly larger if right ventricular function was normal, rather than abnormal, at presentation (+2.55 years, 95% CI: −0.03, +3.97 vs. +0.98 years, 95% CI: +0.47, +2.00, p = 0.04).ConclusionPDE5i treatment in ILD-PH should be investigated by a prospective randomized trial.

Journal article

Howard LSGE, He J, Watson GMJ, Huang L, Wharton J, Luo Q, Kiely DG, Condliffe R, Pepke-Zaba J, Morrell NW, Sheares KK, Ulrich A, Quan R, Zhao Z, Jing X, An C, Liu Z, Xiong C, Robbins PA, Dawes T, de MA, Rhodes CJ, Richter MJ, Gall H, Ghofrani HA, Zhao L, Huson L, Wilkins MRet al., 2022, Supplementation with Iron in Pulmonary Arterial Hypertension: Two Randomized Crossover Trials (vol 18, pg 981, 2021), ANNALS OF THE AMERICAN THORACIC SOCIETY, Vol: 19, Pages: 703-703, ISSN: 1546-3222

Journal article

Simoes Monteiro de Marvao A, McGurk K, Zheng S, Thanaj M, Bai W, Duan J, Biffi C, Mazzarotto F, Statton B, Dawes T, Savioli N, Halliday B, Xu X, Buchan R, Baksi A, Quinlan M, Tokarczuk P, Tayal U, Francis C, Whiffin N, Theotokis A, Zhang X, Jang M, Berry A, Pantazis A, Barton P, Rueckert D, Prasad S, Walsh R, Ho C, Cook S, Ware J, O'Regan Det al., 2021, Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy, Journal of the American College of Cardiology, Vol: 78, Pages: 1097-1110, ISSN: 0735-1097

Background: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomereencoding genes, but little is known about the clinical significance of these variants in thegeneral population.Objectives: To compare lifetime outcomes and cardiovascular phenotypes according to thepresence of rare variants in sarcomere-encoding genes amongst middle-aged adults.Methods: We analysed whole exome sequencing and cardiac magnetic resonance (CMR)imaging in UK Biobank participants stratified by sarcomere-encoding variant status.Results: The prevalence of rare variants (allele frequency <0.00004) in HCM-associatedsarcomere-encoding genes in 200,584 participants was 2.9% (n=5,712; 1 in 35), and theprevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was0.25% (n=493, 1 in 407). SARC-HCM-P/LP variants were associated with increased risk ofdeath or major adverse cardiac events compared to controls (HR 1.69, 95% CI 1.38 to 2.07,p<0.001), mainly due to heart failure endpoints (HR 4.23, 95% CI 3.07 to 5.83, p<0.001). In21,322 participants with CMR, SARC-HCM-P/LP were associated with asymmetric increasein left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p<0.001) buthypertrophy (≥13mm) was only present in 18.4% (n=9/49, 95% CI 9 to 32%). SARC-HCMP/LP were still associated with heart failure after adjustment for wall thickness (HR 6.74,95% CI 2.43 to 18.7, p<0.001).Conclusions: In this population of middle-aged adults, SARC-HCM-P/LP variants have lowaggregate penetrance for overt HCM but are associated with increased risk of adversecardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absoluteevent rates are low, identification of these variants may enhance risk stratification beyondfamilial disease.

Journal article

Dawes T, Dimopoulos K, Mccabe C, Bax S, Kempny A, Molyneaux P, George P, Kouranos V, Chua F, Renzoni E, Kokosi M, Wells AU, Wort SJ, Price LCet al., 2021, Survival effects of pulmonary vasodilators in group 3 pulmonary hypertension, European-Respiratory-Society (ERS) International Congress, Publisher: EUROPEAN RESPIRATORY SOC JOURNALS LTD, ISSN: 0903-1936

Conference paper

Howard LSGE, He J, Watson GMJ, Huang L, Wharton J, Luo Q, Kiely DG, Condliffe R, Pepke-Zaba J, Morrell NW, Sheares KK, Ulrich A, Quan R, Zhao Z, Jing X, An C, Liu Z, Xiong C, Robbins PA, Dawes T, de Marvao A, Rhodes CJ, Richter MJ, Gall H, Ghofrani HA, Zhao L, Huson L, Wilkins MRet al., 2021, Supplementation with iron in pulmonary arterial hypertension: two randomized crossover trials., Annals of the American Thoracic Society, Vol: 18, Pages: 981-988, ISSN: 1546-3222

RATIONALE: Iron deficiency, in the absence of anaemia, is common in patients with idiopathic and heritable pulmonary arterial hypertension (PAH) and is associated with a worse clinical outcome. Oral iron absorption may be impeded by elevated circulating hepcidin levels. The safety and benefit of parenteral iron replacement in this patient population is unclear. OBJECTIVES: To evaluate the safety and efficacy of parenteral iron replacement in pulmonary arterial hypertension. METHODS: In two randomised, double blind, placebo-controlled 12 week crossover studies, 39 patients in Europe received a single infusion of ferric carboxymaltose (Ferinject®) 1000 mg (or 15 mg/kg if weight < 66.7Kg) or saline as placebo and 17 patients in China received iron dextran (Cosmofer®) 20 mg iron/kg body weight or saline placebo. All patients had idiopathic or heritable PAH and iron deficiency at entry as defined by: a serum ferritin < 37 µg/l or iron < 10.3 µmol/l or transferrin saturations < 16.4%. RESULTS: Both iron treatments were well tolerated and improved iron status. Analysed separately and combined, there was no effect on any measure of exercise capacity (using cardiopulmonary exercise testing or 6 minute walk test) or cardio-pulmonary haemodynamics, as assessed by right heart catheterisation, cardiac magnetic resonance or plasma NT-proBNP, at 12 weeks. CONCLUSION: Iron repletion by administration of a slow release iron preparation as a single infusion to PAH patients with iron deficiency without overt anaemia was well tolerated but provided no significant clinical benefit at 12 weeks. Clinical trial registered with ClinicalTrials.gov (NCT01447628).

Journal article

de Marvao A, McGurk KA, Zheng SL, Thanaj M, Bai W, Duan J, Biffi C, Mazzarotto F, Statton B, Dawes TJW, Savioli N, Halliday BP, Xu X, Buchan RJ, Baksi AJ, Quinlan M, Tokarczuk P, Tayal U, Francis C, Whiffin N, Theotokis PI, Zhang X, Jang M, Berry A, Pantazis A, Barton PJR, Rueckert D, Prasad SK, Walsh R, Ho CY, Cook SA, Ware JS, ORegan DPet al., 2021, Outcomes and phenotypic expression of rare variants in hypertrophic cardiomyopathy genes amongst UK Biobank participants

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We compared outcomes and cardiovascular phenotypes in UK Biobank participants with whole exome sequencing stratified by sarcomere-encoding variant status.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The prevalence of rare variants (allele frequency &lt;0.00004) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n=5,727; 1 in 35), of which 0.24% (n=474, 1 in 423) were pathogenic or likely pathogenic variants (SARC-P/LP). SARC-P/LP variants were associated with increased risk of death or major adverse cardiac events compared to controls (HR 1.68, 95% CI 1.37-2.06, p&lt;0.001), mainly due to heart failure (HR 4.40, 95% CI 3.22-6.02, p&lt;0.001) and arrhythmia (HR 1.55, 95% CI 1.18-2.03, p=0.002). In 21,322 participants with cardiac magnetic resonance imaging, SARC-P/LP were associated with increased left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p&lt;0.001) and concentric remodelling (mass/volume ratio: 0.63±0.12 vs 0.58±0.09 g/mL, p&lt;0.001), but hypertrophy (≥13mm) was only present in 16% (n=7/43, 95% CI 7-31%). Other rare sarcomere-encoding variants had a weak effect on wall thickness (9.5±1.7 vs 9.4±1.6 mm, p=0.002) with no combined excess cardiovascular risk (HR 1.00 95% CI 0.92-1.08, p=0.9).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>In the general population, SARC-P/LP variants have low aggregate penetrance for overt HCM bu

Working paper

Meyer H, Dawes T, Serrani M, Bai W, Tokarczuk P, Cai J, Simoes Monteiro de Marvao A, Henry A, Lumbers T, Gierten J, Thumberger T, Wittbrodt J, Ware J, Rueckert D, Matthews P, Prasad S, Costantino M, Cook S, Birney E, O'Regan Det al., 2020, Genetic and functional insights into the fractal structure of the heart, Nature, Vol: 584, Pages: 589-594, ISSN: 0028-0836

The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a vestigeof embryonic development.1,2 The function of these trabeculae in adults and their genetic architecture are unknown. Toinvestigate this we performed a genome-wide association study using fractal analysis of trabecular morphology as animage-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associatedwith haemodynamic phenotypes and regulation of cytoskeletal arborisation.3,4 Using biomechanical simulations and humanobservational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Throughgenetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationshipbetween trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardialtrabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal theirinfluence on susceptibility to disease

Journal article

Bhuva AN, Treibel TA, De Marvao A, Biffi C, Dawes TJW, Doumou G, Bai W, Patel K, Boubertakh R, Rueckert D, O'Regan DP, Hughes AD, Moon JC, Manisty CHet al., 2020, Sex and regional differences inmyocardial plasticity in aortic stenosis are revealed by 3D modelmachine learning, EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, Vol: 21, Pages: 417-427, ISSN: 2047-2404

Journal article

Mazzarotto F, Tayal U, Buchan RJ, Midwinter W, Wilk A, Whiffin N, Govind R, Mazaika E, de Marvao A, Dawes T, Felkin LE, Ahmad M, Theotokis PI, Edwards E, Ing AI, Thomson KL, Chan LLH, Sim D, Baksi AJ, Pantazis A, Roberts AM, Watkins H, Funke B, O'Regan D, Olivotto I, Barton PJR, Prasad SK, Cook SA, Ware JS, Walsh Ret al., 2020, Re-evaluating the genetic contribution of monogenic dilated cardiomyopathy, Circulation, Vol: 141, Pages: 387-398, ISSN: 0009-7322

Background: Dilated cardiomyopathy (DCM) is genetically heterogeneous, with >100 purported disease genes tested in clinical laboratories. However, many genes were originally identified based on candidate-gene studies that did not adequately account for background population variation. Here we define the frequency of rare variation in 2538 DCM patients across protein-coding regions of 56 commonly tested genes and compare this to both 912 confirmed healthy controls and a reference population of 60,706 individuals in order to identify clinically interpretable genes robustly associated with dominant monogenic DCM.Methods: We used the TruSight Cardio sequencing panel to evaluate the burden of rare variants in 56 putative DCM genes in 1040 DCM patients and 912 healthy volunteers processed with identical sequencing and bioinformatics pipelines. We further aggregated data from 1498 DCM patients sequenced in diagnostic laboratories and the ExAC database for replication and meta-analysis.Results: Truncating variants in TTN and DSP were associated with DCM in all comparisons. Variants in MYH7, LMNA, BAG3, TNNT2, TNNC1, PLN, ACTC1, NEXN, TPM1 and VCL were significantly enriched in specific patient subsets, with the last 2 genes potentially contributing primarily to early-onset forms of DCM. Overall, rare variants in these 12 genes potentially explained 17% of cases in the outpatient clinic cohort representing a broad range of adult DCM patients and 26% of cases in the diagnostic referral cohort enriched in familial and early-onset DCM. Whilst the absence of a significant excess in other genes cannot preclude a limited role in disease, such genes have limited diagnostic value since novel variants will be uninterpretable and their diagnostic yield is minimal.Conclusion: In the largest sequenced DCM cohort yet described, we observe robust disease association with 12 genes, highlighting their importance in DCM and translating into high interpretability in diagnostic testing. The

Journal article

de Marvao A, Dawes TJ, Howard JP, O'Regan DPet al., 2020, Artificial intelligence and the cardiologist: what you need to know for 2020., Heart, Vol: 106, Pages: 399-400, ISSN: 1355-6037

Journal article

de Marvao A, Dawes TJW, O'Regan DP, 2020, Artificial intelligence for cardiac imaging-genetics research, Frontiers in Cardiovascular Medicine, Vol: 6, Pages: 1-10, ISSN: 2297-055X

Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.

Journal article

Jin S, Savioli N, Marvao AD, Dawes TJW, Gandy A, Rueckert D, O'Regan DPet al., 2019, Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network, Publisher: arXiv

In this work, a novel approach is proposed for joint analysis of highdimensional time-resolved cardiac motion features obtained from segmentedcardiac MRI and low dimensional clinical risk factors to improve survivalprediction in heart failure. Different methods are evaluated to find theoptimal way to insert conventional covariates into deep prediction networks.Correlation analysis between autoencoder latent codes and covariate features isused to examine how these predictors interact. We believe that similarapproaches could also be used to introduce knowledge of genetic variants tosuch survival networks to improve outcome prediction by jointly analysingcardiac motion traits with inheritable risk factors.

Working paper

Duan J, Bello G, Schlemper J, Bai W, Dawes TJW, Biffi C, Marvao AD, Doumou G, O'Regan DP, Rueckert Det al., 2019, Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach, IEEE Transactions on Medical Imaging, Vol: 38, Pages: 2151-2164, ISSN: 0278-0062

Deep learning approaches have achieved state-of-the-art performance incardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular3D models, despite the artefacts in input CMR volumes.

Journal article

Attard M, Dawes T, Simoes Monteiro de Marvao A, Biffi C, Shi W, Wharton J, Rhodes C, Ghataorhe P, Gibbs J, Howard L, Rueckert D, Wilkins M, O'Regan Det al., 2019, Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: Three dimensional analysis of cardiac magnetic resonance imaging, EHJ Cardiovascular Imaging / European Heart Journal - Cardiovascular Imaging, Vol: 20, Pages: 668-676, ISSN: 2047-2412

AimsWe sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress.Methods and resultsIn 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function—including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = −0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28–0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = −0.40) of sulfated androgen.ConclusionUsing computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.

Journal article

Mazzarotto F, Tayal P, Buchan R, Midwinter W, Wilk A, Whiffin N, Govind R, Mazaika E, De Marvao A, Felkin L, Dawes T, Ahmad M, Edwards E, Ing A, Thomson K, Chan L, Sim D, Baksi J, Pantazis A, Roberts A, Watkins H, Funke B, O'Regan D, Olivotto I, Barton P, Prasad S, Cook S, Ware J, Walsh Ret al., 2019, RE-EVALUATING THE GENETIC CONTRIBUTION OF MONOGENIC DILATED CARDIOMYOPATHY, Annual Conference of the British-Cardiovascular-Society (BCS) - Digital Health Revolution, Publisher: BMJ PUBLISHING GROUP, Pages: A100-A100, ISSN: 1355-6037

Conference paper

Meyer HV, Dawes TJW, Serrani M, Bai W, Tokarczuk P, Cai J, de Marvao A, Rueckert D, Matthews PM, Costantino ML, Birney E, Cook SA, ORegan DPet al., 2019, Genomic analysis reveals a functional role for myocardial trabeculae in adults

<jats:title>ABSTRACT</jats:title><jats:p>Since being first described by Leonardo da Vinci in 1513 it has remained an enigma why the endocardial surfaces of the adult heart retain a complex network of muscular trabeculae – with their persistence thought to be a vestige of embryonic development. For causative physiological inference we harness population genomics, image-based intermediate phenotyping and <jats:italic>in silico</jats:italic> modelling to determine the effect of this complex cardiovascular trait on function. Using deep learning-based image analysis we identified genetic associations with trabecular complexity in 18,097 UK Biobank participants which were replicated in an independently measured cohort of 1,129 healthy adults. Genes in these associated regions are enriched for expression in the fetal heart or vasculature and implicate loci associated with haemodynamic phenotypes and developmental pathways. A causal relationship between increasing trabecular complexity and both ventricular performance and electrical activity are supported by complementary biomechanical simulations and Mendelian randomisation studies. These findings show that myocardial trabeculae are a previously-unrecognised determinant of cardiovascular physiology in adult humans.</jats:p>

Working paper

Bello G, Dawes T, Duan J, Biffi C, Simoes Monteiro de Marvao A, Howard L, Gibbs S, Wilkins M, Cook S, Rueckert D, O'Regan Det al., 2019, Deep learning cardiac motion analysis for human survival prediction, Nature Machine Intelligence, Vol: 1, Pages: 95-104, ISSN: 2522-5839

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C-index) was significantly higher (P = 0.0012) for our model C = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.

Journal article

Duan J, Schlemper J, Bai W, Dawes TJW, Bello G, Biffi C, Doumou G, De Marvao A, O’Regan DP, Rueckert Det al., 2018, Combining deep learning and shape priors for bi-ventricular segmentation of volumetric cardiac magnetic resonance images, MICCAI ShapeMI Workshop, Publisher: Springer Verlag, Pages: 258-267, ISSN: 0302-9743

In this paper, we combine a network-based method with image registration to develop a shape-based bi-ventricular segmentation tool for short-axis cardiac magnetic resonance (CMR) volumetric images. The method first employs a fully convolutional network (FCN) to learn the segmentation task from manually labelled ground truth CMR volumes. However, due to the presence of image artefacts in the training dataset, the resulting FCN segmentation results are often imperfect. As such, we propose a second step to refine the FCN segmentation. This step involves performing a non-rigid registration with multiple high-resolution bi-ventricular atlases, allowing the explicit shape priors to be inferred. We validate the proposed approach on 1831 healthy subjects and 200 subjects with pulmonary hypertension. Numerical experiments on the two datasets demonstrate that our approach is capable of producing accurate, high-resolution and anatomically smooth bi-ventricular models, despite the artefacts in the input CMR volumes.

Conference paper

Dawes T, Simoes Monteiro de Marvao A, Shi W, Rueckert D, Cook S, O'Regan Det al., 2018, Identifying the optimal regional predictor of right ventricular global function: a high resolution 3D cardiac magnetic resonance study, Anaesthesia, Vol: 74, Pages: 312-320, ISSN: 0003-2409

Right ventricular (RV) function has prognostic value in acute, chronic and peri‐operative disease, although the complex RV contractile pattern makes rapid assessment difficult. Several two‐dimensional (2D) regional measures estimate RV function, however the optimal measure is not known. High‐resolution three‐dimensional (3D) cardiac magnetic resonance cine imaging was acquired in 300 healthy volunteers and a computational model of RV motion created. Points where regional function was significantly associated with global function were identified and a 2D, optimised single‐point marker (SPM‐O) of global function developed. This marker was prospectively compared with tricuspid annular plane systolic excursion (TAPSE), septum‐freewall displacement (SFD) and their fractional change (TAPSE‐F, SFD‐F) in a test cohort of 300 patients in the prediction of RV ejection fraction. RV ejection fraction was significantly associated with systolic function in a contiguous 7.3 cm2 patch of the basal RV freewall combining transverse (38%), longitudinal (35%) and circumferential (27%) contraction and coinciding with the four‐chamber view. In the test cohort, all single‐point surrogates correlated with RV ejection fraction (p < 0.010), but correlation (R) was higher for SPM‐O (R = 0.44, p < 0.001) than TAPSE (R = 0.24, p < 0.001) and SFD (R = 0.22, p < 0.001), and non‐significantly higher than TAPSE‐F (R = 0.40, p < 0.001) and SFD‐F (R = 0.43, p < 0.001). SPM‐O explained more of the observed variance in RV ejection fraction (19%) and predicted it more accurately than any other 2D marker (median error 2.8 ml vs 3.6 ml, p < 0.001). We conclude that systolic motion of the basal RV freewall predicts global function more accurately than other 2D estimators. However, no markers summarise 3D contractile patterns, limiting their predictive accuracy.

Journal article

Duan J, Schlemper J, Bai W, Dawes TJW, Bello G, Doumou G, De Marvao A, O'Regan DP, Rueckert Det al., 2018, Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Pages: 595-603, ISSN: 0302-9743

Conference paper

Dawes T, Cai J, Quinlan M, Simoes Monteiro de Marvao A, Ostowski P, Tokarczuk P, Watson G, Wharton J, Howard L, Gibbs J, Cook S, Wilkins M, O'Regan DPet al., 2018, Fractal analysis of right ventricular trabeculae in pulmonary hypertension, Radiology, Vol: 288, Pages: 386-395, ISSN: 0033-8419

Purpose: To measure right ventricular (RV) trabecular complexity by its fractal dimension (FD) in healthy subjects and patients with pulmonary hypertension (PH) and assess its relationship to hemodynamic and functional parameters, and future cardiovascular events. Materials and methods: This retrospective study used data acquired from May 2004 to October 2013 for 256 patients with newly-diagnosed PH that underwent cardiac magnetic resonance (CMR) imaging, right heart catheterization and six-minute walk distance testing with a median follow-up of 4.0 years. 256 healthy controls underwent CMR only. Biventricular FD, volumes and function were assessed on short-axis cine images. Reproducibility was assessed by intraclass correlation coefficient, correlation between variables was assessed by Pearson’s correlation test, and mortality prediction compared by univariable and multivariable Cox regression analysis. Results: RV-FD reproducibility had an intraclass correlation coefficient of 0.97 (95% confidence interval [CI]: 0.96, 0.98).RV-FD was higher in PH patients than healthy subjects (median 1.310, inter-quartile range [IQR] 1.281-1.341 vs 1.264, 1.242-1.295, P<.001) with the greatest difference near the apex. RV-FD was associated with pulmonary vascular resistance (r=0.30, P<.001). In univariable Cox regression analysis, RV-FD was a significant predictor of death (hazards ratio [HR]: 1.256, CI: 1.011, 1.560, P=.04), but in a multivariable analysis did not predict survival independently of conventional parameters of RV remodeling (HR: 1.179, CI: 0.871, 1.596, P=0.29). Conclusion: Fractal analysis of RV trabecular complexity is a highly reproducible measure of remodeling in PH associated with afterload, although the gain in survival prediction over traditional markers is not significant.

Journal article

Bhuva A, Treibel TA, De Marvao A, Biffi C, Dawes T, Doumou G, Bai W, Oktay O, Jones S, Davies R, Chaturvedi N, Rueckert D, Hughes A, Moon JC, Manisty CHet al., 2018, Septal hypertrophy in aortic stenosis and its regression after valve replacement is more plastic in males than females: insights from 3D machine learning approach, European-Society-of-Cardiology Congress, Publisher: European Society of Cardiology, Pages: 1132-1132, ISSN: 0195-668X

Background: Evaluation of left ventricular non-compaction (LVNC) is an increasingly common indication for cardiac magnetic resonance imaging (MRI). Fractal dimension (FD) is a unitless measure of geometrical complexity which can be used to quantify LV trabeculation. FD is increased in LVNC, but there have been few studies on FD in normal subjects. The aim of the study was to establish reference ranges for FD in a healthy population, and identify covariates which are associated with FD.Methods: MRI was performed in 1,913 volunteers without hypertension, diabetes, or heart disease (1055 female, 858 male; median age 40, range 19-82). FD was derived from LV short-axis images, using a custom MATLAB box-counting algorithm. The maximal FD in the apical half of the LV was used for all analyses, as previously described.Results: Normal ranges (2.5-97.5th percentile) for female and male subjects were 1.154 - 1.367 and 1.179 - 1.392, respectively. FD was significantly correlated with age, gender, ethnicity, body surface area (BSA), activity score, and systolic blood pressure. In multivariable analysis, FD was independently correlated with increased age (β 0.11, p<0.001), male gender (β 0.09, p<0.001), African/Afro-Caribbean ethnicity (β 0.18, p<0.001), increased BSA (β 0.27, p<0.001), and increased activity score (β 0.07, p=0.002). Since ethnicity was found to significantly affect FD, normal ranges were calculated for each subgroup (see table).Conclusions: This is the largest study on FD in healthy subjects, and the first to present gender- and race-specific normal ranges. The association between FD and age suggests that LV trabeculation is a dynamic phenotype which may change with age.

Conference paper

Dawes TJW, Serrani M, Bai W, Cai J, Suzuki H, de Marvao A, Quinlan M, Tokarczuk P, Ostrowski P, Matthews P, Rueckert D, Cook S, Costantino ML, O'Regan Det al., 2018, Myocardial trabeculae improve left ventricular function: a combined UK Biobank and computational analysis, GAT Annual Scientific Meeting 2018, Publisher: Association of Anaesthetists of Great Britain and Ireland

Conference paper

de Marvao A, Biffi C, Walsh R, Doumou G, Dawes T, Shi W, Bai W, Berry A, Buchan R, Pierce I, Tokarczuk P, Statton B, Francis C, Duan J, Quinlan M, Felkin L, Le T-T, Bhuva A, Tang HC, Barton P, Chin CW-L, Rueckert D, Ware J, Prasad S, O'Regan DP, Cook SAet al., 2018, Defining The Effects Of Genetic Variation Using Machine Learning Analysis Of CMRs: A Study In Hypertrophic Cardiomyopathy And In A Healthy Population, Joint Meeting of the British-Society-of-Cardiovascular-Imaging/British-Society-of-Cardiovascular-CT, British-Society-of-Cardiovascular-Magnetic-Resonance and British-Nuclear-Cardiac-Society on British Cardiovascular Imaging, Publisher: BMJ PUBLISHING GROUP, Pages: A7-A8, ISSN: 1355-6037

Conference paper

Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook S, de Marvao A, Dawes T, O'Regan D, Kainz B, Glocker B, Rueckert Det al., 2018, Anatomically Constrained Neural Networks (ACNN): application to cardiac image enhancement and segmentation, IEEE Transactions on Medical Imaging, Vol: 37, Pages: 384-395, ISSN: 0278-0062

Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learnt deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.

Journal article

Biffi C, Simoes Monteiro de Marvao A, Attard M, Dawes T, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook S, Rueckert D, O'Regan DPet al., 2017, Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework, Bioinformatics, ISSN: 1367-4803

Motivation: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for highthroughput mapping of genotype-phenotype associations in three dimensions (3D).Results: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts.Availability: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work.

Journal article

Dawes T, de Marvao A, Shi W, Rueckert D, Cook S, O'Regan Det al., 2017, Systolic motion of the basal right ventricular freewall is the strongest predictor of global function: a high resolution 3D imaging study, Association-of-Anaesthetists-of-Great-Britain-and-Ireland (AAGBI) GAT Annual Scientific Meeting, Publisher: Wiley, Pages: 77-77, ISSN: 0003-2409

Conference paper

Dawes TJW, Price S, 2017, Tranexamic Acid in Patients Undergoing Coronary-Artery Surgery, Publisher: MASSACHUSETTS MEDICAL SOC

Other

Dawes T, Simoes monteiro de marvao A, Shi W, Fletcher T, Watson G, Wharton J, Rhodes C, Howard L, Gibbs J, Rueckert D, Cook S, Wilkins M, O'Regan DPet al., 2017, Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study, Radiology, Vol: 283, Pages: 381-390, ISSN: 1527-1315

Purpose: To determine if patient survival and mechanisms of right ventricular (RV) failure in pulmonary hypertension (PH) could be predicted using supervised machine learning of three dimensional patterns of systolic cardiac motion. Materials and methods: The study was approved by a research ethics committee and participants gave written informed consent. 256 patients (143 females, mean age 63 ± 17) with newly diagnosed PH underwent cardiac MR imaging, right heart catheterization (RHC) and six minute walk testing (6MWT) with a median follow up of 4.0 years. Semi automated segmentation of short axis cine images was used to create a three dimensional model of right ventricular motion. Supervised principal components analysis identified patterns of systolic motion which were most strongly predictive of survival. Survival prediction was assessed by the difference in median survival time and the area under the curve (AUC) using time dependent receiver operator characteristic for one year survival. Results: At the end of follow up 33% (93/256) died and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and freewall coupled with reduced basal longitudinal motion. When added to conventional imaging, hemodynamic, functional and clinical markers, three dimensional cardiac motion improved survival prediction (area under the curve 0.73 vs 0.60, p<0.001) and provided greater differentiation by difference in median survival time between high and low risk groups (13.8 vs 10.7 years, p<0.001). Conclusion:Three dimensional motion modeling with machine learning approaches reveal the adaptations in function that occur early in right heart failure and independently predict outcomes in newly diagnosed PH patients.

Journal article

Schafer S, de Marvao A, Adami E, Fiedler LR, Ng B, Khin E, Rackham O, van Heesch S, Pua CJ, Kui M, Walsh R, Tayal U, Prasad SK, Dawes TJW, Ko NSJ, Sim D, Chan LLH, Chin CWL, Mazzarotto F, Barton PJ, Kreuchwig F, de Kleijn DPV, Totman T, Biffi C, Tee N, Rueckert D, Schneider V, Faber A, Regitz-Zagrosek V, Seidman JG, Seidman CE, Linke WA, Kovalik J, O'Regan D, Ware JS, Hubner N, Cook SAet al., 2017, Titin-truncating variants affect heart function in disease cohorts and the general population, Nature Genetics, Vol: 49, Pages: 46-53, ISSN: 1546-1718

Titin-truncating variants (TTNtv) commonly cause dilated cardiomyopathy (DCM). TTNtv are also encountered in ~1% of the general population, where they may be silent, perhaps reflecting allelic factors. To better understand TTNtv, we integrated TTN allelic series, cardiac imaging and genomic data in humans and studied rat models with disparate TTNtv. In patients with DCM, TTNtv throughout titin were significantly associated with DCM. Ribosomal profiling in rat showed the translational footprint of premature stop codons in Ttn, TTNtv-position-independent nonsense-mediated degradation of the mutant allele and a signature of perturbed cardiac metabolism. Heart physiology in rats with TTNtv was unremarkable at baseline but became impaired during cardiac stress. In healthy humans, machine-learning-based analysis of high-resolution cardiac imaging showed TTNtv to be associated with eccentric cardiac remodeling. These data show that TTNtv have molecular and physiological effects on the heart across species, with a continuum of expressivity in health and disease.

Journal article

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