The Future of Cardiac Imaging

Douglas, Pamela S.; Cerqueira, Manuel D.; Berman, Daniel S.; Chinnaiyan, Kavitha M.; Lundbye, Justin; Patel, Rajan A.G.; Sengupta, Partho P.; Soman, Prem; Weissman, Neil J.; Wong, Timothy C.; Asch, Federico M.; Bateman, Timothy M.; Biesbrock, Ginger; Brinker, Jeffrey A.; Brophy, David; Cohen, Meryl S.; Dilsizian, Vasken; Dudley, J.; Epstein, Frederick H.; Gillam, Linda D.; McCallister, Ben D.; Narula, Jagat; Reynolds, David; Schoepf, U. Joseph; Shah, Nishant; Udelson, James E.; Valeti, Uma; Walsh, Mary Norine; Ward, R. Parker; Weigold, Wm. Guy; White, Richard D.; Wolinsky, David G.; Zoghbi, William A.

doi:10.1016/j.jcmg.2016.02.027

Cited by 42 publications

(7 citation statements)

References 40 publications

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“…Imaging plays a central part in precision health initiatives allowing the early detection of oncological and systemic disease 63 . In our study, imaging helped detect dilated cardiomyopathy (in the RBM20 patient), early-stage atherosclerotic disease and a case of asymptomatic lymphoma.…”

Section: Discussionmentioning

confidence: 99%

A longitudinal big data approach for precision health

Rose

Contrepois

Moneghetti

et al. 2019

Nat Med

362

271

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Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways, and impact behavior in a prospective longitudinal cohort ( n = 109) enriched for risk of type 2 diabetes mellitus (DM). The cohort underwent integrative Personalized Omics Profiling (iPOP) from samples collected quarterly for up to 8 years (median 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome, and wearable monitoring. We discovered over 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance using omics measurements illustrating their potential to replace burdensome tests. Finally, study participation lead the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.

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Section: Discussionmentioning

confidence: 99%

A longitudinal big data approach for precision health

Rose

Contrepois

Moneghetti

et al. 2019

Nat Med

362

271

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“…There are major geographical and inter-hospital variations in the use of diagnostic imaging which cannot be explained by differences in the prevalence of disease. Many of the principles that we should adopt when developing collaborative multimodality imaging have been described, 117 , 118 but they remain insufficiently implemented. Our subspecialty is much more than ‘imaging’; our challenge is to make it better clinical science.…”

Section: Discussionmentioning

confidence: 99%

A manifesto for cardiovascular imaging: addressing the human factor†

Fraser

2017

European Heart Journal - Cardiovascular Imaging

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Our use of modern cardiovascular imaging tools has not kept pace with their technological development. Diagnostic errors are common but seldom investigated systematically. Rather than more impressive pictures, our main goal should be more precise tests of function which we select because their appropriate use has therapeutic implications which in turn have a beneficial impact on morbidity or mortality. We should practise analytical thinking, use checklists to avoid diagnostic pitfalls, and apply strategies that will reduce biases and avoid overdiagnosis. We should develop normative databases, so that we can apply diagnostic algorithms that take account of variations with age and risk factors and that allow us to calculate pre-test probability and report the post-test probability of disease. We should report the imprecision of a test, or its confidence limits, so that reference change values can be considered in daily clinical practice. We should develop decision support tools to improve the quality and interpretation of diagnostic imaging, so that we choose the single best test irrespective of modality. New imaging tools should be evaluated rigorously, so that their diagnostic performance is established before they are widely disseminated; this should be a shared responsibility of manufacturers with clinicians, leading to cost-effective implementation. Trials should evaluate diagnostic strategies against independent reference criteria. We should exploit advances in machine learning to analyse digital data sets and identify those features that best predict prognosis or responses to treatment. Addressing these human factors will reap benefit for patients, while technological advances continue unpredictably.

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“…Fully agreeing with a recent State of Art Paper, dealing with hot issues about the future of imaging and developing a roadmap to address critical challenges,[3] we propose “translational cardiovascular imaging” as a new approach for the evaluation of cardiac remodeling processes: A comprehensive evaluation of cardiac structure and function through innovative imaging techniques (i.e., 3D-echocardiography and speckle tracking as well as in vivo tissue characterization by CMR) together with the assessment of some specific biomarkers of cardiac remodeling. The objective is the redefinition of the role of cardiovascular imaging, oriented to an integrated (imaging and biohumoral) approach for a better assessment of cardiac pathophysiology, hopefully helping with risk stratification and clinical management.…”

mentioning

confidence: 94%