Imperial College London

ProfessorDeclanO'Regan

Faculty of MedicineInstitute of Clinical Sciences

Professor of Imaging Sciences
 
 
 
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Contact

 

+44 (0)20 3313 1510declan.oregan

 
 
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Location

 

Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Publication Type
Year
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193 results found

Kasapi M, Xu K, Ebbels T, O'Regan D, Ware J, Posma JMet al., 2024, LAVASET: An ensemble method for correlated datasets with spatial, spectral, and temporal dependencies., Bioinformatics, ISSN: 1367-4803

Motivation: Random Forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction (DR). However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting. Addressing correlation relationships in high dimensional datasets is imperative for reducing the number of variables that are assigned high importance, hence making the DR most efficient. Here, we propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each feature and aims to incorporate the correlation factor in the splitting step.Results: Without compromising on performance in the majority of examples, LAVASET outperforms RF by accurately determining feature importance across all correlated variables and ensuring proper distribution of importance values. LAVASET yields mostly non-inferior prediction accuracies to traditional RFs when tested in simulated and real 1D datasets, as well as more complex and high-dimensional 3D datatypes. Unlike traditional RFs, LAVASET is unaffected by single `important' noisy features (false positives), as it considers the local neighbourhood. LAVASET, therefore, highlights neighbourhoods of features, reflecting real signals that collectively impact the model's predictive ability.Availability: LAVASET is freely available as a standalone package from https://github.com/melkasapi/LAVASET.

Journal article

Hall M, de Marvao A, Schweitzer R, Cromb D, Colford K, Jandu P, O'Regan DP, Ho A, Price A, Chappell LC, Rutherford MA, Story L, Lamata P, Hutter Jet al., 2024, Preeclampsia Associated Differences in the Placenta, Fetal Brain, and Maternal Heart Can Be Demonstrated Antenatally: An Observational Cohort Study Using MRI., Hypertension

BACKGROUND: Preeclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multiorgan approach to magnetic resonance imaging (MRI) investigation of preeclampsia, with the acquisition of maternal cardiac, placental, and fetal brain anatomic and functional imaging. METHODS: An observational study was performed recruiting 3 groups of pregnant women: those with preeclampsia, chronic hypertension, or no medical complications. All women underwent a cardiac MRI, and pregnant women underwent a placental-fetal MRI. Cardiac analysis for structural, morphological, and flow data were undertaken; placenta and fetal brain volumetric and T2* (which describes relative tissue oxygenation) data were obtained. All results were corrected for gestational age. A nonpregnant cohort was identified for inclusion in the statistical shape analysis. RESULTS: Seventy-eight MRIs were obtained during pregnancy. Cardiac MRI analysis demonstrated higher left ventricular mass in preeclampsia with 3-dimensional modeling revealing additional specific characteristics of eccentricity and outflow track remodeling. Pregnancies affected by preeclampsia demonstrated lower placental and fetal brain T2*. Within the preeclampsia group, 23% placental T2* results were consistent with controls, these were the only cases with normal placental histopathology. Fetal brain T2* results were consistent with normal controls in 31% of cases. CONCLUSIONS: We present the first holistic assessment of the immediate implications of preeclampsia on maternal heart, placenta, and fetal brain. As well as having potential clinical implications for the risk stratification and management of women with preeclampsia, this gives an insight into the disease mechanism.

Journal article

Lip G, O'Regan DP, 2024, Can machine learning predict cardiac risk using mammography?, European Heart Journal - Cardiovascular Imaging, ISSN: 2047-2404

Journal article

Meng Q, Bai W, O'Regan DP, Rueckert Det al., 2023, DeepMesh: mesh-based cardiac motion tracking using deep learning, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current state-of-the art methods focus on estimating dense pixel-/voxel-wise motion fields in image space, which ignores the fact that motion estimation is only relevant and useful within the anatomical objects of interest, e.g., the heart. In this work, we model the heart as a 3D mesh consisting of epi- and endocardial surfaces. We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects. In DeepMesh, the heart mesh of the end-diastolic frame of an individual subject is first reconstructed from the template mesh. Mesh-based 3D motion fields with respect to the end-diastolic frame are then estimated from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, DeepMesh is able to leverage 2D shape information from multiple anatomical views for 3D mesh reconstruction and mesh motion estimation. The proposed method estimates vertex-wise displacement and thus maintains vertex correspondences between time frames, which is important for the quantitative assessment of cardiac function across different subjects and populations. We evaluate DeepMesh on CMR images acquired from the UK Biobank. We focus on 3D motion estimation of the left ventricle in this work. Experimental results show that the proposed method quantitatively and qualitatively outperforms other image-based and mesh-based cardiac motion tracking methods.

Journal article

Curran L, Simoes Monteiro de Marvao A, Inglese P, McGurk K, Schiratti P-R, Clement A, Zheng S, Li S, Pua CJ, Shah M, Jafari M, Theotokis P, Buchan R, Jurgens S, Raphael C, Baksi A, Pantazis A, Halliday B, Pennell D, Bai W, Chin C, Tadros R, Bezzina C, Watkins H, Cook S, Prasad S, Ware J, O'Regan Det al., 2023, Genotype-phenotype taxonomy of hypertrophic cardiomyopathy, Circulation: Genomic and Precision Medicine, Vol: 16, Pages: 559-570, ISSN: 2574-8300

Background:Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes but there is no systematic framework for classifying morphology or assessing associated risks. Here we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression.Methods:We enrolled 436 HCM patients (median age 60 years; 28.8% women) with clinical, genetic and imaging data. Anindependent cohort of 60 HCM patients from Singapore (median age 59 years; 11% women) and a reference population from UK Biobank (n = 16,691, mean age 55 years; 52.5% women) were also recruited. We used machine learning to analyse the three-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree.Results:Carriers of pathogenic or likely pathogenic variants (P/LP) for HCM had lower left ventricular mass, but greater basalseptal hypertrophy, with reduced lifespan (mean follow-up 9.9 years) compared to genotype negative individuals(hazard ratio: 2.66; 95% confidence interval [CI]: 1.42-4.96; P < 0.002). Four main phenotypic branches were identified using unsupervised learning of three-dimensional shape: 1) non-sarcomeric hypertrophy with co-existing hypertension; 2) diffuse and basal asymmetric hypertrophy associated with outflow tract obstruction; 3) isolated basal hypertrophy; 4) milder non-obstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for P/LP variants: 2.18 [95% CI: 1.93-2.28, P = 0.0001]). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalisable to an independent cohort (trustworthiness M1: 0.86-0.88).Conclusions:We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severi

Journal article

Qiao M, Wang S, Qiu H, Marvao AD, O'Regan D, Rueckert D, Bai Wet al., 2023, CHeart: a conditional spatio-temporal generative model for cardiac anatomy, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can often be addressed by image segmentation and motion tracking algorithms, our capability to model and answer the second question is still limited. In this work, we propose a novel conditional generative model to describe the 4D spatio-temporal anatomy of the heart and its interaction with non-imaging clinical factors. The clinical factors are integrated as the conditions of the generative modelling, which allows us to investigate how these factors influence the cardiac anatomy. We evaluate the model performance in mainly two tasks, anatomical sequence completion and sequence generation. The model achieves high performance in anatomical sequence completion, comparable to or outperforming other state-of-the-art generative models. In terms of sequence generation, given clinical conditions, the model can generate realistic synthetic 4D sequential anatomies that share similar distributions with the real data. We will share the code and the trained generative model at https://github.com/MengyunQ/CHeart.

Journal article

C-MOREPHOSP-COVID Collaborative Group, 2023, Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study, The Lancet Respiratory Medicine, Vol: 11, Pages: 1003-1019, ISSN: 2213-2600

INTRODUCTION: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. METHODS: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. FINDINGS: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2-6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MR

Journal article

Jones RE, Hammersley DJ, Zheng S, McGurk KA, de Marvao A, Theotokis PI, Owen R, Tayal U, Rea G, Hatipoglu S, Buchan RJ, Mach L, Curran L, Lota AS, Simard F, Reddy RK, Talukder S, Yoon WY, Vazir A, Pennell DJ, O'Regan DP, Baksi AJ, Halliday BP, Ware JS, Prasad SKet al., 2023, Assessing the association between genetic and phenotypic features of dilated cardiomyopathy and outcome in patients with coronary artery disease, European Journal of Heart Failure, ISSN: 1388-9842

AimsTo examine the relevance of genetic and cardiovascular magnetic resonance (CMR) features of dilated cardiomyopathy (DCM) in individuals with coronary artery disease (CAD).Methods and resultsThis study includes two cohorts. First, individuals with CAD recruited into the UK Biobank (UKB) were evaluated. Second, patients with CAD referred to a tertiary centre for evaluation with late gadolinium enhancement (LGE)-CMR were recruited (London cohort); patients underwent genetic sequencing as part of the research protocol and long-term follow-up. From 31 154 individuals with CAD recruited to UKB, rare pathogenic variants in DCM genes were associated with increased risk of death or major adverse cardiac events (hazard ratio 1.57, 95% confidence interval [CI] 1.22–2.01, p < 0.001). Of 1619 individuals with CAD included from the UKB CMR substudy, participants with a rare variant in a DCM-associated gene had lower left ventricular ejection fraction (LVEF) compared to genotype negative individuals (mean 47 ± 10% vs. 57 ± 8%, p < 0.001). Of 453 patients in the London cohort, 63 (14%) had non-infarct pattern LGE (NI-LGE) on CMR. Patients with NI-LGE had lower LVEF (mean 38 ± 18% vs. 48 ± 16%, p < 0.001) compared to patients without NI-LGE, with no significant difference in the burden of rare protein altering variants in DCM-associated genes between groups (9.5% vs. 6.7%, odds ratio 1.5, 95% CI 0.4–4.3, p = 0.4). NI-LGE was not independently associated with adverse clinical outcomes.ConclusionRare pathogenic variants in DCM-associated genes impact left ventricular remodelling and outcomes in stable CAD. NI-LGE is associated with adverse remodelling but is not an independent predictor of outcome and had no rare genetic basis in our study.

Journal article

McGurk K, Zhang X, Theotokis P, Thomson K, Harper A, Buchan R, Mazaika E, Ormondroyd E, Wright W, Macaya D, Chee Jian P, Funke B, MacArthur D, Prasad S, Cook S, Allouba M, Aguib Y, Yacoub M, O'Regan D, Barton P, Watkins H, Bottolo L, Ware Jet al., 2023, The penetrance of rare variants in cardiomyopathy-associated genes: a cross-sectional approach to estimate penetrance for secondary findings, American Journal of Human Genetics, Vol: 110, Pages: 1482-1495, ISSN: 0002-9297

Understanding the penetrance of pathogenic variants identified as secondary findings (SFs) is of paramount importance with the growing availability of genetic testing. We estimated penetrance through large-scale analyses of individuals referred for diagnostic sequencing for hypertrophic cardiomyopathy (HCM; 10,400 affected individuals, 1,332 variants) and dilated cardiomyopathy (DCM; 2,564 affected individuals, 663 variants), using a cross-sectional approach comparing allele frequencies against reference populations (293,226 participants from UK Biobank and gnomAD). We generated updated prevalence estimates for HCM (1:543) and DCM (1:220). In aggregate, the penetrance by late adulthood of rare, pathogenic variants (23% for HCM, 35% for DCM) and likely pathogenic variants (7% for HCM, 10% for DCM) was substantial for dominant cardiomyopathy (CM). Penetrance was significantly higher for variant subgroups annotated as loss of function or ultra-rare and for males compared to females for variants in HCM-associated genes. We estimated variant-specific penetrance for 316 recurrent variants most likely to be identified as SFs (found in 51% of HCM- and 17% of DCM-affected individuals). 49 variants were observed at least ten times (14% of affected individuals) in HCM-associated genes. Median penetrance was 14.6% (±14.4% SD). We explore estimates of penetrance by age, sex, and ancestry and simulate the impact of including future cohorts. This dataset reports penetrance of individual variants at scale and will inform the management of individuals undergoing genetic screening for SFs. While most variants had low penetrance and the costs and harms of screening are unclear, some individuals with highly penetrant variants may benefit from SFs.

Journal article

Shah M, Inacio M, Lu C, Schiratti P-R, Zheng S, Clement A, Simoes Monteiro de Marvao A, Bai W, King A, Ware J, Wilkins M, Mielke J, Elci E, Kryukov I, McGurk K, Bender C, Freitag D, O'Regan Det al., 2023, Environmental and genetic predictors of human cardiovascular ageing, Nature Communications, Vol: 14, Pages: 1-15, ISSN: 2041-1723

Cardiovascular ageing is a process that begins early in life and leads to a progressive change instructure and decline in function due to accumulated damage across diverse cell types, tissues andorgans contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence andend-organ damage, however the genetic architecture of cardiovascular ageing is not known. Herewe use machine learning approaches to quantify cardiovascular age from image-derived traits ofvascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated bycardiometabolic risk factors and we also identify prescribed medications that are potential modifiersof ageing. Through large-scale modelling of ageing across multiple traits our results reveal insightsinto the mechanisms driving premature cardiovascular ageing and reveal potential molecular targetsto attenuate age-related processes.

Journal article

Salmasi MY, Pirola S, Mahuttanatan S, Fisichella SM, Sengupta S, Jarral OA, Oo A, O'Regan D, Xu XY, Athanasiou Tet al., 2023, Geometry and flow in ascending aortic aneurysms are influenced by left ventricular outflow tract orientation: Detecting increased wall shear stress on the outer curve of proximal aortic aneurysms, JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY, Vol: 166, Pages: 11-+, ISSN: 0022-5223

Journal article

Hanna L, Gibbs RGJ, London Aortic Mechanobiology Working Group, 2023, Type II Endoleaks and Culprit Vessels: Will 4D MRI Change the Paradigm?, Eur J Vasc Endovasc Surg, Vol: 66

Journal article

McGurk K, Santofimio V, Clement A, ORegan D, Ware Jet al., 2023, Identification of an increased lifetime risk of major adverse cardiovascular events in UK Biobank participants with scoliosis, Open Heart, Vol: 10, Pages: 1-8, ISSN: 2053-3624

Background Structural changes caused by spinal curvature may impact the organs within the thoracic cage, including the heart. Cardiac abnormalities in patients with idiopathic scoliosis are often studied post-corrective surgery or secondary to diseases. To investigate cardiac structure, function and outcomes in participants with scoliosis, phenotype and imaging data of the UK Biobank (UKB) adult population cohort were analysed.Methods Hospital episode statistics of 502 324 adults were analysed to identify participants with scoliosis. Summary 2D cardiac phenotypes from 39 559 cardiac MRI (CMR) scans were analysed alongside a 3D surface-to-surface (S2S) analysis.Results A total of 4095 (0.8%, 1 in 120) UKB participants were identified to have all-cause scoliosis. These participants had an increased lifetime risk of major adverse cardiovascular events (MACEs) (HR=1.45, p<0.001), driven by heart failure (HR=1.58, p<0.001) and atrial fibrillation (HR=1.54, p<0.001). Increased radial and decreased longitudinal peak diastolic strain rates were identified in participants with scoliosis (+0.29, Padj <0.05; −0.25, Padj <0.05; respectively). Cardiac compression of the top and bottom of the heart and decompression of the sides was observed through S2S analysis. Additionally, associations between scoliosis and older age, female sex, heart failure, valve disease, hypercholesterolemia, hypertension and decreased enrolment for CMR were identified.Conclusion The spinal curvature observed in participants with scoliosis alters the movement of the heart. The association with increased MACE may have clinical implications for whether to undertake surgical correction. This work identifies, in an adult population, evidence for altered cardiac function and an increased lifetime risk of MACE in participants with scoliosis.

Journal article

Saitta S, Maga L, Armour C, Votta E, O'Regan DP, Salmasi MY, Athanasiou T, Weinsaft JW, Xu XY, Pirola S, Redaelli Aet al., 2023, Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta., Computer Methods and Programs in Biomedicine, Vol: 233, Pages: 1-8, ISSN: 0169-2607

BACKGROUND AND OBJECTIVE: Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. METHODS: Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. RESULTS: Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. CONCLUSIONS: We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational stu

Journal article

Siguero-Álvarez M, Salguero-Jiménez A, Grego-Bessa J, de la Barrera J, MacGrogan D, Prados B, Sánchez-Sáez F, Piñeiro-Sabarís R, Felipe-Medina N, Torroja C, José Gómez M, Sabater-Molina M, Escribá R, Richaud-Patin I, Iglesias-García O, Sbroggio M, Callejas S, O'Regan DP, McGurk KA, Dopazo A, Giovinazzo G, Ibañez B, Monserrat L, María Pérez-Pomares J, Sánchez-Cabo F, Pendas AM, Raya A, Gimeno-Blanes JR, de la Pompa JLet al., 2023, Human hereditary cardiomyopathy shares a genetic substrate with bicuspid aortic valve., Circulation, Vol: 147, Pages: 47-65, ISSN: 0009-7322

BACKGROUND: The complex genetics underlying human cardiac disease is evidenced by its heterogenous manifestation, multigenic basis, and sporadic occurrence. These features have hampered disease modeling and mechanistic understanding. Here, we show that 2 structural cardiac diseases, left ventricular noncompaction (LVNC) and bicuspid aortic valve, can be caused by a set of inherited heterozygous gene mutations affecting the NOTCH ligand regulator MIB1 (MINDBOMB1) and cosegregating genes. METHODS: We used CRISPR-Cas9 gene editing to generate mice harboring a nonsense or a missense MIB1 mutation that are both found in LVNC families. We also generated mice separately carrying these MIB1 mutations plus 5 additional cosegregating variants in the ASXL3, APCDD1, TMX3, CEP192, and BCL7A genes identified in these LVNC families by whole exome sequencing. Histological, developmental, and functional analyses of these mouse models were carried out by echocardiography and cardiac magnetic resonance imaging, together with gene expression profiling by RNA sequencing of both selected engineered mouse models and human induced pluripotent stem cell-derived cardiomyocytes. Potential biochemical interactions were assayed in vitro by coimmunoprecipitation and Western blot. RESULTS: Mice homozygous for the MIB1 nonsense mutation did not survive, and the mutation caused LVNC only in heteroallelic combination with a conditional allele inactivated in the myocardium. The heterozygous MIB1 missense allele leads to bicuspid aortic valve in a NOTCH-sensitized genetic background. These data suggest that development of LVNC is influenced by genetic modifiers present in affected families, whereas valve defects are highly sensitive to NOTCH haploinsufficiency. Whole exome sequencing of LVNC families revealed single-nucleotide gene variants of ASXL3, APCDD1, TMX3, CEP192, and BCL7A cosegregating with the MIB1 mutations and LVNC. In experiments with mice harboring the orthologous variants on the corres

Journal article

Salmasi MY, Alwis S, Cyclewala S, Jarral OA, Mohamed H, Mozalbat D, Nienaber CA, Athanasiou T, Morris-Rosendahl D, Members of the London Aortic Mechanobiology Working Groupet al., 2023, The genetic basis of thoracic aortic disease: The future of aneurysm classification?, Hellenic Journal of Cardiology, Vol: 69, Pages: 41-50, ISSN: 1109-9666

The expansion in the repertoire of genes linked to thoracic aortic aneurysms (TAA) has revolutionised our understanding of the disease process. The clinical benefits of such progress are numerous, particularly helping our understanding of non-syndromic hereditary causes of TAA (HTAAD) and further refinement in the subclassification of disease. Furthermore, the understanding of aortic biomechanics and mechanical homeostasis has been significantly informed by the discovery of deleterious mutations and their effect on aortic phenotype. The drawbacks in genetic testing in TAA lie with the inability to translate genotype to accurate prognostication in the risk of thoracic aortic dissection (TAD), which is a life-threatening condition. Under current guidelines, there are no metrics by which those at risk for dissection with normal aortic diameters may undergo preventive surgery. Future research lies with more advanced genetic diagnosis of HTAAD and investigation of the diverse pathways involved in its pathophysiology, which will i) serve to improve our understanding of the underlying mechanisms, ii) improve guidelines for treatment and iii) prevent complications for HTAAD and sporadic aortopathies.

Journal article

Patel K, Bajaj N, Statton B, Li X, Herath NS, Nyamakope K, Davidson R, Stoks J, Purkayastha S, Ware JS, O'Regan DP, Lambiase PD, Cluitmans M, Peters NS, Ng FSet al., 2022, Bariatric surgery reverses ventricular repolarisation heterogeneity in obesity: mechanistic insights into fat-related arrhythmic risk, ESC Congress 2022, Publisher: Oxford University Press, Pages: 658-658, ISSN: 1554-2815

Conference paper

Mallon DH, McNamara CD, Rahmani GS, O'Regan DP, Amiras DGet al., 2022, Automated detection of enteric tubes misplaced in the respiratory tract on chest radiographs using deep learning with two centre validation, Clinical Radiology, Vol: 77, Pages: e758-e764, ISSN: 0009-9260

AIM: To develop and test a model based on a convolutional neural network that can identify enteric tube position accurately on chest radiography. MATERIALS AND METHODS: The chest radiographs of adult patients were classified by radiologists based on enteric tube position as either critically misplaced (within the respiratory tract) or not critically misplaced (misplaced within the oesophagus or safely positioned below the diaphragm). A deep-learning model based on the 121-layer DenseNet architecture was developed using a training and validation set of 4,693 chest radiographs. The model was evaluated on an external test data set from a separate institution that consisted of 1,514 consecutive radiographs with a real-world incidence of critically misplaced enteric tubes. RESULTS: The receiver operator characteristic area under the curve was 0.90 and 0.92 for the internal validation and external test data sets, respectively. For the external data set with a prevalence of 4.4% of critically misplaced enteric tubes, the model achieved high accuracy (92%), sensitivity (80%), and specificity (92%) for identifying a critically misplaced enteric tube. The negative predictive value (99%) was higher than the positive predictive value (32%). CONCLUSION: The present study describes the development and external testing of a model that accurately identifies an enteric tube misplaced within the respiratory tract. This model could help reduce the risk of the catastrophic consequences of feeding through a misplaced enteric tube.

Journal article

Alabed S, Alandejani F, Dwivedi K, Karunasaagarar K, Sharkey M, Garg P, de Koning PJH, Tóth A, Shahin Y, Johns C, Mamalakis M, Stott S, Capener D, Wood S, Metherall P, Rothman AMK, Condliffe R, Hamilton N, Wild JM, O'Regan DP, Lu H, Kiely DG, van der Geest RJ, Swift AJet al., 2022, Validation of artificial intelligence cardiac MRI measurements: relationship to heart catheterization and mortality prediction, Radiology, Vol: 305, ISSN: 0033-8419

Background Cardiac MRI measurements have diagnostic and prognostic value in the evaluation of cardiopulmonary disease. Artificial intelligence approaches to automate cardiac MRI segmentation are emerging but require clinical testing. Purpose To develop and evaluate a deep learning tool for quantitative evaluation of cardiac MRI functional studies and assess its use for prognosis in patients suspected of having pulmonary hypertension. Materials and Methods A retrospective multicenter and multivendor data set was used to develop a deep learning-based cardiac MRI contouring model using a cohort of patients suspected of having cardiopulmonary disease from multiple pathologic causes. Correlation with same-day right heart catheterization (RHC) and scan-rescan repeatability was assessed in prospectively recruited participants. Prognostic impact was assessed using Cox proportional hazard regression analysis of 3487 patients from the ASPIRE (Assessing the Severity of Pulmonary Hypertension In a Pulmonary Hypertension Referral Centre) registry, including a subset of 920 patients with pulmonary arterial hypertension. The generalizability of the automatic assessment was evaluated in 40 multivendor studies from 32 centers. Results The training data set included 539 patients (mean age, 54 years ± 20 [SD]; 315 women). Automatic cardiac MRI measurements were better correlated with RHC parameters than were manual measurements, including left ventricular stroke volume (r = 0.72 vs 0.68; P = .03). Interstudy repeatability of cardiac MRI measurements was high for all automatic measurements (intraclass correlation coefficient range, 0.79-0.99) and similarly repeatable to manual measurements (all paired t test P > .05). Automated right ventricle and left ventricle cardiac MRI measurements were associated with mortality in patients suspected of having pulmonary hypertension. Conclusion An automatic cardiac MRI measurement approach was developed and tested in a large cohort of pa

Journal article

Sharkey MJ, Taylor JC, Alabed S, Dwivedi K, Karunasaagarar K, Johns CS, Rajaram S, Garg P, Alkhanfar D, Metherall P, O'Regan DP, van der Geest RJ, Condliffe R, Kiely DG, Mamalakis M, Swift AJet al., 2022, Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning, Frontiers in Cardiovascular Medicine, Vol: 9, Pages: 1-18, ISSN: 2297-055X

Introduction: Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA.Methods: A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort.Results: Dice similarity coefficients (DSC) for segmented structures were in the range 0.58–0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785–0.801) and 0.520 (0.482–0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar

Journal article

Alabed S, Alandejani F, Dwivedi K, Karunasaagarar K, Sharkey M, Garg P, de Koning PJH, Tóth A, Shahin Y, Johns C, Mamalakis M, Stott S, Capener D, Wood S, Metherall P, Rothman AMK, Condliffe R, Hamilton N, Wild JM, O'Regan DP, Lu H, Kiely DG, van der Geest RJ, Swift AJet al., 2022, Validation of artificial intelligence cardiac MRI measurements: relationship to heart catheterization and mortality prediction., Radiology, Vol: 304, Pages: 1-1, ISSN: 0033-8419

Journal article

Meng Q, Bai W, Liu T, Simoes Monteiro de Marvao A, O'Regan D, Rueckert Det al., 2022, MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI, IEEE Transactions on Medical Imaging, Vol: 41, Pages: 1961-1974, ISSN: 0278-0062

Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.

Journal article

Alabed S, Maiter A, Salehi M, Mahmood A, Daniel S, Jenkins S, Goodlad M, Sharkey M, Mamalakis M, Rakocevic V, Dwivedi K, Assadi H, Wild JMM, Lu H, O'Regan DPP, van der Geest RJJ, Garg P, Swift AJJet al., 2022, Quality of reporting in AI cardiac MRI segmentation studies - A systematic review and recommendations for future studies, Frontiers in Cardiovascular Medicine, Vol: 9, Pages: 1-10, ISSN: 2297-055X

Background: There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation.Methods: MEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains.Results: 209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59–73%). Median compliance was highest for the model description domain (100%, IQR 80–100%) and lower for the study (71%, IQR 63–86%), dataset (63%, IQR 50–67%) and performance (60%, IQR 50–70%) description domains.Conclusion: This systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing—most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis—that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence

Journal article

Tayal U, Verdonschot JAJ, Hazebroek MR, Howard J, Gregson J, Newsome S, Gulati A, Pua CJ, Halliday BP, Lota AS, Buchan RJ, Whiffin N, Kanapeckaite L, Baruah R, Jarman JWE, O'Regan DP, Barton PJR, Ware JS, Pennell DJ, Adriaans BP, Bekkers SCAM, Donovan J, Frenneaux M, Cooper LT, Januzzi JL, Cleland JGF, Cook SA, Deo RC, Heymans SRB, Prasad SKet al., 2022, Precision phenotyping of dilated cardiomyopathy using multidimensional data., Journal of the American College of Cardiology, Vol: 79, Pages: 2219-2232, ISSN: 0735-1097

BACKGROUND: Dilated cardiomyopathy (DCM) is a final common manifestation of heterogenous etiologies. Adverse outcomes highlight the need for disease stratification beyond ejection fraction. OBJECTIVES: The purpose of this study was to identify novel, reproducible subphenotypes of DCM using multiparametric data for improved patient stratification. METHODS: Longitudinal, observational UK-derivation (n = 426; median age 54 years; 67% men) and Dutch-validation (n = 239; median age 56 years; 64% men) cohorts of DCM patients (enrolled 2009-2016) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments. Machine learning with profile regression identified novel disease subtypes. Penalized multinomial logistic regression was used for validation. Nested Cox models compared novel groupings to conventional risk measures. Primary composite outcome was cardiovascular death, heart failure, or arrhythmia events (median follow-up 4 years). RESULTS: In total, 3 novel DCM subtypes were identified: profibrotic metabolic, mild nonfibrotic, and biventricular impairment. Prognosis differed between subtypes in both the derivation (P < 0.0001) and validation cohorts. The novel profibrotic metabolic subtype had more diabetes, universal myocardial fibrosis, preserved right ventricular function, and elevated creatinine. For clinical application, 5 variables were sufficient for classification (left and right ventricular end-systolic volumes, left atrial volume, myocardial fibrosis, and creatinine). Adding the novel DCM subtype improved the C-statistic from 0.60 to 0.76. Interleukin-4 receptor-alpha was identified as a novel prognostic biomarker in derivation (HR: 3.6; 95% CI: 1.9-6.5; P = 0.00002) and validation cohorts (HR: 1.94; 95% CI: 1.3-2.8; P = 0.00005). CONCLUSIONS: Three reproducible, mechanistically distinct DCM subtypes were identified using widely available clinical and biological data, adding prognostic value to trad

Journal article

Patel K, Bajaj N, Statton B, Herath N, Li X, Davidson R, Savvidou S, Coghlin J, Stoks J, Purkayastha S, Cousins J, Ware J, O'Regan D, Lambiase P, Cluitmans M, Peters N, Ng FSet al., 2022, Bariatric surgery reverses ventricular repolarisation heterogeneity – mechanistic insights into fat-related arrhythmic risk, British Cardiovascular Society Annual Conference, ‘100 years of Cardiology’, 6–8 June 2022, Publisher: BMJ Publishing Group, Pages: A60-A61, ISSN: 1355-6037

Conference paper

Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P, DECIDE-AI expert groupet al., 2022, Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI, BMJ: British Medical Journal, Vol: 377, ISSN: 0959-535X

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.

Journal article

Thanaj M, Mielke J, McGurk K, Bai W, Savioli N, Simoes Monteiro de Marvao A, Meyer H, Zeng L, Sohler F, Lumbers T, Wilkins M, Ware J, Bender C, Rueckert D, MacNamara A, Freitag D, O'Regan Det al., 2022, Genetic and environmental determinants of diastolic heart function, Nature Cardiovascular Research, Vol: 1, Pages: 361-371, ISSN: 2731-0590

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends onmyocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processesand is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiacmotion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wideassociation study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomericfunction under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes wereindependent predictors of diastolic function and we found a causal relationship between genetically-determined ventricularstiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolicfunction that are relevant for identifying causal relationships and potential tractable targets.

Journal article

Manchester E, Pirola S, Salmasi MY, O'Regan D, Athanasiou T, Xu Xet al., 2022, Evaluation of computational methodologies for accurate prediction of wall shear stress and turbulence parameters in a patient-specific aorta, Frontiers in Bioengineering and Biotechnology, Vol: 10, ISSN: 2296-4185

Background: Recent studies suggest that blood flow in main arteries is intrinsically disturbed, even under healthy conditions. Despite this, many computational fluid dynamics (CFD) analyses of aortic haemodynamics make the assumption of laminar flow, and best practices surroundingappropriate modelling choices are lacking. This study aims to address this gap by evaluating different modelling and post-processing approaches in simulations of a patient-specific aorta. Methods: Magnetic resonance imaging (MRI) and 4D flow MRI from a patient with aortic valve stenosis were used to reconstruct the aortic geometry and derive patient-specific inlet and outlet boundary conditions. Three different computational approaches were considered based on assumed laminar or assumed disturbed flow states including low-resolution laminar (LR-laminar),high-resolution laminar (HR-Laminar) and large-eddy simulation (LES). Each simulation was ran for 30 cardiac cycles and post-processing was conducted on either the final cardiac cycle, or using a phase-averaged approach which utilised all 30 simulated cycles. Model capabilities were evaluated in terms of mean and turbulence-based parameters. Results: All simulation types, regardless of post-processing approach could correctly predict velocity values and flow patterns throughout the aorta. Lower resolution simulations could not accurately predict gradient-derived parameters including wall shear stress and viscous energy loss (largest differences up to 44.6% and 130.3%, respectively), although phase-averagingthese parameters improved predictions. The HR-Laminar simulation produced more comparable results to LES with largest differences in wall shear stress and viscous energy loss parameters up to 5.1% and 11.6%, respectively. Laminar-based parameters were better estimated thanturbulence-based parameters.Conclusions: Our findings suggest that well-resolved laminar simulations can accurately predict many laminar-based parameters in disturbed flo

Journal article

McGurk KA, Zheng SL, Henry A, Josephs K, Edwards M, de Marvao A, Whiffin N, Roberts A, Lumbers TR, O'Regan DP, Ware JSet al., 2022, Correspondence on "ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG)" by Miller et al, Genetics in Medicine, Vol: 24, Pages: 744-746, ISSN: 1098-3600

Journal article

Meng Q, Bai W, Liu T, Simoes Monteiro de Marvao A, O'Regan D, Rueckert Det al., 2022, Multiview Motion Estimation for 3D cardiac motion tracking

Code for paper ''MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI''

Software

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