Dataset Information

Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance.

ABSTRACT: Background: Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA.

Methods: 1.5 T CMR was performed in 206 subjects with suspected CA (n?=?100, 49% with unexplained left ventricular (LV) hypertrophy; n?=?106, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients were randomly assigned to the training (n?=?134, 65%), validation (n?=?30, 15%), and testing subgroups (n?=?42, 20%). Short axis, 2-chamber, 4-chamber late gadolinium enhancement (LGE) images were evaluated by 3 networks (DL algorithms). The tags "amyloidosis present" or "absent" were attributed when the average probability of CA from the 3 networks was???50% or?
Results: The DL strategy displayed good diagnostic accuracy (88%), with an area under the curve (AUC) of 0.982. The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 83%, 95%, and 89% respectively. A ML algorithm considering all CMR features had a similar diagnostic yield to DL strategy (AUC 0.952 vs. 0.982; p?=?0.39).

Conclusions: A DL approach evaluating LGE acquisitions displayed a similar diagnostic performance for CA to a ML-based approach, which simulates CMR reading by experienced operators.

SUBMITTER: Martini N

PROVIDER: S-EPMC7720569 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance.

Martini Nicola N Aimo Alberto A Barison Andrea A Della Latta Daniele D Vergaro Giuseppe G Aquaro Giovanni Donato GD Ripoli Andrea A Emdin Michele M Chiappino Dante D

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 20201207 1

<h4>Background</h4>Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA.<h4>Methods</h4>1.5 T CMR was performed in 206 subjects with suspected CA (n = 100, 49% with unexplained left ventricular (LV) hypertrophy; n = 106, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients we ...[more]

PMID: 33287829

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Project description:BackgroundWhile patients with cardiac transthyretin amyloidosis are easily diagnosed with bone scintigraphy, the detection of cardiac light chain (AL) amyloidosis is challenging. Cardiac magnetic resonance (CMR) analyses play an essential role in the differential diagnosis of cardiomyopathies; however, limited data are available from cardiac AL-Amyloidosis. Hence, the purpose of the present study was to analyze the potential role of CMR in the detection of cardiac AL-amyloidosis.MethodsWe included 35 patients with proved cardiac AL-amyloidosis and two control groups constituted by 330 patients with hypertrophic cardiomyopathy (HCM) and 70 patients with arterial hypertension (HT), who underwent CMR examination. The phenotype and degree of left ventricular (LV) hypertrophy and the amount and pattern of late gadolinium enhancement (LGE) were evaluated. In addition, global and regional LV strain parameters were also analyzed using feature-tracking techniques. Sensitivity and specificity of several CMR parameters were analyzed in diagnosing cardiac AL-amyloidosis.ResultsThe sensitivity and specificity of diffuse septal subendocardial LGE in diagnosing cardiac AL-amyloidosis was 88% and 100%, respectively. Likewise, the sensitivity and specificity of septal myocardial nulling prior to blood pool was 71% and 100%, respectively. In addition, a LV end-diastolic septal wall thickness ≥ 15 mm had an optimal diagnostic performance to differentiate cardiac AL-amyloidosis from HT (sensitivity 91%, specificity 89%). On the other hand, a reduced global LV longitudinal strain (< 15%) plus apical sparing (apex-to-base longitudinal strain > 2) had a very low sensitivity (6%) in detecting AL-Amyloidosis, but with very high specificity (100%).ConclusionsThe findings from this study suggest that CMR could have an optimal diagnostic performance in the diagnosis of cardiac AL-amyloidosis. Hence, further larger studies are warranted to validate the findings from this study.

Project description:Background Cardiac amyloidosis (CA) is a disease of interstitial myocardial infiltration, usually by light chains or transthyretin. We used diffusion tensor cardiovascular magnetic resonance (DT-CMR) to noninvasively assess the effects of amyloid infiltration on the cardiac microstructure. Methods DT-CMR was performed at diastole and systole in 20 CA, 11 hypertrophic cardiomyopathy, and 10 control subjects with calculation of mean diffusivity, fractional anisotropy, and sheetlet orientation (secondary eigenvector angle). Results Mean diffusivity was elevated and fractional anisotropy reduced in CA compared with both controls and hypertrophic cardiomyopathy (P<0.001). In CA, mean diffusivity was correlated with extracellular volume (r=0.68, P=0.004), and fractional anisotropy was inversely correlated with circumferential strain (r=-0.65, P=0.02). In CA, diastolic secondary eigenvector angle was elevated, and secondary eigenvector angle mobility was reduced compared with controls (both P<0.001). Diastolic secondary eigenvector angle was correlated with amyloid burden measured by extracellular volume in transthyretin, but not light chain amyloidosis. Conclusions DT-CMR can characterize the microstructural effects of amyloid infiltration and is a contrast-free method to identify the location and extent of the expanded disorganized myocardium. The diffusion biomarkers mean diffusivity and fractional anisotropy effectively discriminate CA from hypertrophic cardiomyopathy. DT-CMR demonstrated that failure of sheetlet relaxation in diastole correlated with extracellular volume in transthyretin, but not light chain amyloidosis. This indicates that different mechanisms may be responsible for impaired contractility in CA, with an amyloid burden effect in transthyretin, but an idiosyncratic effect in light chain amyloidosis. Consequently, DT-CMR offers a contrast-free tool to identify novel pathophysiology, improve diagnostics, and monitor disease through noninvasive microstructural assessment.

Project description:BackgroundIn-vivo cardiovascular magnetic resonance (CMR) diffusion tensor imaging (DTI) allows imaging of alterations of cardiac fiber architecture in diseased hearts. Cardiac amyloidosis (CA) causes myocardial infiltration of misfolded proteins with unknown consequences for myocardial microstructure. This study applied CMR DTI in CA to assess microstructural alterations and their consequences for myocardial function compared to healthy controls.MethodsTen patients with CA (8 AL, 2 ATTR) and ten healthy controls were studied using a diffusion-weighed second-order motion-compensated spin-echo sequence at 1.5 T. Additionally, left ventricular morphology, ejection fraction, strain and native T1 values were obtained in all subjects. In CA patients, T1 mapping was repeated after the administration of gadolinium for extracellular volume fraction (ECV) calculation. CMR DTI analysis was performed to yield the scalar diffusion metrics mean diffusivity (MD) and fractional anisotropy (FA) as well as the characteristics of myofiber orientation including helix, transverse and E2A sheet angle (HA, TA, E2A).ResultsMD and FA were found to be significantly different between CA patients and healthy controls (MD 1.77 ± 0.17 10- 3 vs 1.41 ± 0.07 10- 3 mm2/s, p < 0.001; FA 0.25 ± 0.04 vs 0.35 ± 0.03, p < 0.001). MD demonstrated an excellent correlation with native T1 (r = 0.908, p < 0.001) while FA showed a significant correlation with ECV in the CA population (r = - 0.851, p < 0.002). HA exhibited a more circumferential orientation of myofibers in CA patients, in conjunction with a higher TA standard deviation and a higher absolute E2A sheet angle. The transmural HA slope was found to be strongly correlated with the global longitudinal strain (r = 0.921, p < 0.001).ConclusionCMR DTI reveals significant alterations of scalar diffusion metrics in CA patients versus healthy controls. Elevated MD and lower FA values indicate myocardial disarray with higher diffusion in CA that correlates well with native T1 and ECV measures. In CA patients, CMR DTI showed pronounced circumferential orientation of the myofibers, which may provide the rationale for the reduction of global longitudinal strain that occurs in amyloidosis patients. Accordingly, CMR DTI captures specific features of amyloid infiltration, which provides a deeper understanding of the microstructural consequences of CA.

Project description:BackgroundCardiac amyloidosis (CA) is an infiltrative disease characterised by accumulation of amyloid deposits in the extracellular space of the myocardium-comprising transthyretin (ATTR) and light chain (AL) amyloidosis as the most frequent subtypes. Histopathological proof of amyloid deposits by endomyocardial biopsy (EMB) is the gold standard for diagnosis of CA. Cardiovascular magnetic resonance (CMR) allows non-invasive workup of suspected CA. We conducted a multi-centre study to assess the diagnostic value of CMR in comparison to EMB for the diagnosis of CA.MethodsWe studied N = 160 patients characterised by symptoms of heart failure and presence of left ventricular (LV) hypertrophy of unknown origin who presented to specialised cardiomyopathy centres in Germany and underwent further diagnostic workup by both CMR and EMB. If CA was diagnosed, additional subtyping based on EMB specimens and monoclonal protein studies in serum was performed. The CMR protocol comprised cine- and late-gadolinium-enhancement (LGE)-imaging as well as native and post-contrast T1-mapping (in a subgroup)-allowing to measure extracellular volume fraction (ECV) of the myocardium.ResultsAn EMB-based diagnosis of CA was made in N = 120 patients (CA group) whereas N = 40 patients demonstrated other diagnoses (CONTROL group). In the CA group, N = 114 (95%) patients showed a characteristic pattern of LGE indicative of CA. In the CONTROL group, only 1/40 (2%) patient showed a "false-positive" LGE pattern suggestive of CA. In the CA group, there was no patient with elevated T1-/ECV-values without a characteristic pattern of LGE indicative of CA. LGE-CMR showed a sensitivity of 95% and a specificity of 98% for the diagnosis of CA. The combination of a characteristic LGE pattern indicating CA with unremarkable monoclonal protein studies resulted in the diagnosis of ATTR-CA (confirmed by EMB) with a specificity of 98% [95%-confidence interval (CI) 92-100%] and a positive predictive value (PPV) of 99% (95%-CI 92-100%), respectively. The EMB-associated risk of complications was 3.13% in this study-without any detrimental or persistent complications.ConclusionNon-invasive CMR shows an excellent diagnostic accuracy and yield regarding CA. When combined with monoclonal protein studies, CMR can differentiate ATTR from AL with high accuracy and predictive value. However, invasive EMB remains a safe invasive gold-standard and allows to differentiate CA from other cardiomyopathies that can also cause LV hypertrophy.

Project description:ObjectivesThis study aimed to determine whether texture analysis (TA) and machine learning-based classifications can be applied in differential diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) using non-contrast cine cardiac magnetic resonance (CMR) images.MethodsIn this institutional review board-approved study, we consecutively enrolled 167 patients with CA (n = 85), HCM (n = 82), and 84 patients with normal CMR served as controls. All cases were randomized into training [119 patients (70%)] and validation [48 patients (30%)] groups. A total of 275 texture features were extracted from cine images. Based on regression analysis with the least absolute shrinkage and selection operator (LASSO), nine machine learning models were established and their diagnostic performance determined.ResultsNineteen radiomics texture features derived from cine images were used to differentiate CA and HCM. In the validation cohort, the support vector machine (SVM), which had an accuracy of 0.85, showed the best performance (MCC = 0.637). Gray level non-uniformity (GLevNonU) was the single most effective feature. The combined model of radiomics texture features and conventional MR metrics had superior discriminatory performance (AUC = 0.89) over conventional MR metrics model (AUC = 0.79). Moreover, results showed that GLevNonU levels in HCM patients were significantly higher compared with levels in CA patients and control groups (P < 0.001). A cut-off of GLevNonU ≥ 25 was shown to differentiate between CA and HCM patients, with an area under the curve (AUC) of 0.86 (CI:0.804-0.920). Multiple comparisons tests showed that GLevNonU was significantly greater in LGE+, relative to LGE-patient groups (CA+ vs. CA- and HCM+ vs. HCM-, P = 0.01, 0.001, respectively).ConclusionMachine learning-based classifiers can accurately differentiate between CA and HCM on non-contrast cine images. The radiomics-MR combined model can be used to improve the discriminatory performance. TA may be used to assess myocardial microstructure changes that occur during different stages of cardiomyopathies.

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Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance.

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Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance.

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