An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis

BackgroundVisual interpretation of PET and CMR may fail to identify cardiac sarcoidosis (CS) with high specificity. This study aimed to evaluate the role of [18F]FDG PET and late gadolinium enhancement (LGE)-CMR radiomic features in differentiating CS from another cause of myocardial inflammation, in this case patients with cardiac-related clinical symptoms following COVID-19.Methods[18F]FDG PET and LGE-CMR were treated separately in this work. There were 35 post-COVID-19 (PC) and 40 CS datasets. Regions of interest were delineated manually around the entire left ventricle for the PET and LGE-CMR datasets. Radiomic features were then extracted. The ability of individual features to correctly identify image data as CS or PC was tested to predict the clinical classification of CS vs. PC using Mann–Whitney U-tests and logistic regression. Features were retained if the P-value was <0.00053, the AUC was >0.5, and the accuracy was >0.7. After applying the correlation test, uncorrelated features were used as a signature (joint features) to train machine learning classifiers. For LGE-CMR analysis, to further improve the results, different classifiers were used for individual features besides logistic regression, and the results of individual features of each classifier were screened to create a signature that included all features that followed the previously mentioned criteria and used it them as input for machine learning classifiers.ResultsThe Mann–Whitney U-tests and logistic regression were trained on individual features to build a collection of features. For [18F]FDG PET analysis, the maximum target-to-background ratio (TBRmax) showed a high area under the curve (AUC) and accuracy with small P-values (<0.00053), but the signature performed better (AUC 0.98 and accuracy 0.91). For LGE-CMR analysis, the Gray Level Dependence Matrix (gldm)-Dependence Non-Uniformity showed good results with small error bars (accuracy 0.75 and AUC 0.87). However, by applying a Support Vector Machine classifier to individual LGE-CMR features and creating a signature, a Random Forest classifier displayed better AUC and accuracy (0.91 and 0.84, respectively).ConclusionUsing radiomic features may prove useful in identifying individuals with CS. Some features showed promising results in differentiating between PC and CS. By automating the analysis, the patient management process can be accelerated and improved

Cardiac-Specific Elevations in Thyroid Hormone Enhance Contractility and Prevent Pressure Overload-Induced Cardiac Dysfunction

Thyroid hormone (TH) is critical for cardiac development and heart function. In heart disease, TH metabolism is abnormal, and many biochemical and functional alterations mirror hypothyroidism. Although TH therapy has been advocated for treating heart disease, a clear benefit of TH has yet to be established, possibly because of peripheral actions of TH. To assess the potential efficacy of TH in treating heart disease, type 2 deiodinase (D2), which converts the prohormone thyroxine to active triiodothyronine (T3), was expressed transiently in mouse hearts by using the tetracycline transactivator system. Increased cardiac D2 activity led to elevated cardiac T3 levels and to enhanced myocardial contractility, accompanied by increased Ca(2+) transients and sarcoplasmic reticulum (SR) Ca(2+) uptake. These phenotypic changes were associated with up-regulation of sarco(endo)plasmic reticulum calcium ATPase (SERCA) 2a expression as well as decreased Na(+)/Ca(2+) exchanger, beta-myosin heavy chain, and sarcolipin (SLN) expression. In pressure overload, targeted increases in D2 activity could not block hypertrophy but could completely prevent impaired contractility and SR Ca(2+) cycling as well as altered expression patterns of SERCA2a, SLN, and other markers of pathological hypertrophy. Our results establish that elevated D2 activity in the heart increases T3 levels and enhances cardiac contractile function while preventing deterioration of cardiac function and altered gene expression after pressure overload

Cardiac Insulin Resistance in Subjects With Metabolic Syndrome Traits and Early Subclinical Atherosclerosis.

OBJECTIVE Experimental evidence suggests that metabolic syndrome (MetS) is associated with changes in cardiac metabolism. Whether this association occurs in humans is unknown. RESEARCH DESIGN AND METHODS 821 asymptomatic individuals from the Progression of Early Subclinical Atherosclerosis (PESA) study (50.6 [46.9-53.6] years, 83.7% male) underwent two whole-body 18F-fluorodeoxyglucose positron emission tomography-magnetic resonance (18F-FDG PET-MR) 4.8 ± 0.6 years apart. Presence of myocardial 18F-FDG uptake was evaluated qualitatively and quantitatively. No myocardial uptake was grade 0, while positive uptake was classified in grades 1-3 according to target-to-background ratio tertiles. RESULTS One hundred fifty-six participants (19.0%) showed no myocardial 18F-FDG uptake, and this was significantly associated with higher prevalence of MetS (29.0% vs. 13.9%, P < 0.001), hypertension (29.0% vs. 18.0%, P = 0.002), and diabetes (11.0% vs. 3.2%, P < 0.001), and with higher insulin resistance index (HOMA-IR, 1.64% vs. 1.23%, P < 0.001). Absence of myocardial uptake was associated with higher prevalence of early atherosclerosis (i.e., arterial 18F-FDG uptake, P = 0.004). On follow-up, the associations between myocardial 18F-FDG uptake and risk factors were replicated, and MetS was more frequent in the group without myocardial uptake. The increase in HOMA-IR was associated with a progressive decrease in myocardial uptake (P < 0.001). In 82% of subjects, the categorization according to presence/absence of myocardial 18F-FDG uptake did not change between baseline and follow-up. MetS regression on follow-up was associated with a significant (P < 0.001) increase in myocardial uptake. CONCLUSIONS Apparently healthy individuals without cardiac 18F-FDG uptake have higher HOMA-IR and higher prevalence of MetS traits, cardiovascular risk factors, and early atherosclerosis. An improvement in cardiometabolic profile is associated with the recovery of myocardial 18F-FDG uptake at follow-up.The PESA study is funded by the Centro Nacional de Investigaciones Cardiovasculares (CNIC) and Santander Bank. B.I. is supported by the European Commission (grant numbers 819775 and 945118), by the Spanish Ministry of Science and Innovation (PID2019- 110369RB-I00), and by the Red Madrilena de ~ Nanomedicina en Imagen Molecular-Comunidad de Madrid (S2017/BMD-3867 RENIM-CM). A.D. is an Alfonso Martin Escudero fellow and is scientifically supported by La Caixa Foundation. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovacion (MCIN), and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (grant CEX2020-001041-S funded by MICIN/AEI/ 10.13039/501100011033).S

Hybrid PET- and MR-driven attenuation correction for enhanced ¹⁸F-NaF and ¹⁸F-FDG quantification in cardiovascular PET/MR imaging

Background: The standard MR Dixon-based attenuation correction (AC) method in positron emission tomography/magnetic resonance (PET/MR) imaging segments only the air, lung, fat and soft-tissues (4-class), thus neglecting the highly attenuating bone tissues and affecting quantification in bones and adjacent vessels. We sought to address this limitation by utilizing the distinctively high bone uptake rate constant Ki expected from ¹⁸F-Sodium Fluoride (¹⁸F-NaF) to segment bones from PET data and support 5-class hybrid PET/MR-driven AC for ¹⁸F-NaF and ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) PET/MR cardiovascular imaging. Methods: We introduce 5-class Ki/MR-AC for (i) ¹⁸F-NaF studies where the bones are segmented from Patlak Ki images and added as the 5th tissue class to the MR Dixon 4-class AC map. Furthermore, we propose two alternative dual-tracer protocols to permit 5-class Ki/MR-AC for (ii) ¹⁸F-FDG-only data, with a streamlined simultaneous administration of ¹⁸F-FDG and ¹⁸F-NaF at 4:1 ratio (R4:1), or (iii) for ¹⁸F-FDG-only or both ¹⁸F-FDG and ¹⁸F-NaF dual-tracer data, by administering ¹⁸F-NaF 90 minutes after an equal ¹⁸F-FDG dosage (R1:1). The Ki-driven bone segmentation was validated against computed tomography (CT)-based segmentation in rabbits, followed by PET/MR validation on 108 vertebral bone and carotid wall regions in 16 human volunteers with and without prior indication of carotid atherosclerosis disease (CAD). Results: In rabbits, we observed similar (< 1.2% mean difference) vertebral bone ¹⁸F-NaF SUVmean scores when applying 5-class AC with Ki-segmented bone (5-class Ki/CT-AC) vs CT-segmented bone (5-class CT-AC) tissue. Considering the PET data corrected with continuous CT-AC maps as gold-standard, the percentage SUVmean bias was reduced by 17.6% (¹⁸F-NaF) and 15.4% (R4:1) with 5-class Ki/CT-AC vs 4-class CT-AC. In humans without prior CAD indication, we reported 17.7% and 20% higher ¹⁸F-NaF target-to-background ratio (TBR) at carotid bifurcations wall and vertebral bones, respectively, with 5- vs 4-class AC. In the R4:1 human cohort, the mean ¹⁸F-FDG:¹⁸F-NaF TBR increased by 12.2% at carotid bifurcations wall and 19.9% at vertebral bones. For the R1:1 cohort of subjects without CAD indication, mean TBR increased by 15.3% (¹⁸F-FDG) and 15.5% (¹⁸F-NaF) at carotid bifurcations and 21.6% (¹⁸F-FDG) and 22.5% (¹⁸F-NaF) at vertebral bones. Similar TBR enhancements were observed when applying the proposed AC method to human subjects with prior CAD indication. Conclusions: Ki-driven bone segmentation and 5-class hybrid PET/MR-driven AC is feasible and can significantly enhance ¹⁸F-NaF and ¹⁸F-FDG contrast and quantification in bone tissues and carotid walls

An assessment of PET and CMR radiomic features for detection of cardiac sarcoidosis

Background: Visual interpretation of PET and CMR may fail to identify cardiac sarcoidosis (CS) with high specificity. This study aimed to evaluate the role of [18F]FDG PET and late gadolinium enhancement (LGE)-CMR radiomic features in differentiating CS from another cause of myocardial inflammation, in this case patients with cardiac-related clinical symptoms following COVID-19. Methods: [18F]FDG PET and LGE-CMR were treated separately in this work. There were thirty-five post-COVID-19 (PC) and forty CS datasets. Regions of interest were delineated manually around the entire left ventricle for PET and LGE-CMR datasets. Radiomic features were then extracted. The ability of individual features to correctly identify image data as CS or PC was tested to predict clinical classification of CS vs. PC using Mann–Whitney U-tests and logistic regression. Features were retained if P-value &lt;0.00053, AUC &gt;0.5 and accuracy &gt;0.7. After applying correlation test, uncorrelated features were used as a signature (joint features) to train machine learning classifiers. For LGE-CMR analysis, to further improve the results, different classifiers were used for individual features besides logistic regression and the results of individual features of each classifier were screened to create a signature that include all features that followed the previously mentioned criteria and use them as input for machine learning classifiers. Results: The Mann–Whitney U-tests and logistic regression were trained on individual features to build a collection of features. For [18F]FDG PET analysis, the maximum target-to-background ratio (TBRmax) showed high area under the curve (AUC) and accuracy with small P-values (&lt;0.00053) but the signature performed better (AUC 0.98 and accuracy 0.91). For LGE-CMR analysis, Gray Level Dependence Matrix (gldm)-Dependence Non-Uniformity showed good results with small error bars (accuracy 0.75 and AUC 0.87). However, by applying a Support Vector Machine classifier on individual LGE-CMR features and creating a signature, a Random Forest classifier displayed better AUC and accuracy (0.91 and 0.84, respectively). Conclusion: Using radiomic features may prove useful in identifying individuals with CS. Some features showed promising results to differentiate between PC and CS. By automating the analysis, the patient management process can be accelerated and improved.</p

Cardiac-specific overexpression of sarcolipin in phospholamban null mice impairs myocyte function that is restored by phosphorylation

Sarcolipin (SLN) inhibits the cardiac sarco(endo)plasmic reticulum Ca(2+) ATPase (SERCA2a) by direct binding and is superinhibitory if it binds as a binary complex with phospholamban (PLN). To demonstrate whether overexpression of SLN in the heart might impair cardiac function directly, transgenic (TG) mice with cardiac-specific overexpression of NF-SLN (SLN tagged at its N terminus with the FLAG epitope) were generated on a phospholamban (PLN) null (PLN KO) background. In NF-SLN TG/PLN KO cardiac microsomes, the apparent affinity of SERCA2a for Ca(2+) was decreased compared with non-TG littermate PLN KO hearts. Analyses of isolated NF-SLN/PLN KO cardiomyocytes revealed impaired cardiac contractility, reduced calcium transient peak amplitude, and slower decay kinetics compared to PLN KO animals. In these cardiomyocytes, isoproterenol restored calcium dynamics to the levels seen in PLN KO. Invasive hemodynamic and echocardiographic analyses of NF-SLN/PLN KO mouse cardiac muscle in vivo showed no direct effects of NF-SLN overexpression when compared to PLN KO mice. A possible mechanism for the lack of effects in the whole heart may be a responsiveness to phosphorylation because we determined that NF-SLN can be phosphorylated in cardiomyocytes in response to isoproterenol, and we provide evidence that serine/threonine kinase 16 is a kinase that can phosphorylate NF-SLN. Site-directed mutagenesis showed that SLN Thr-5 is the target site for this kinase. These data show that overexpression of NF-SLN can inhibit SERCA2a in the absence of PLN and that the inhibition of SERCA2a is correlated with impairment of contractility and calcium cycling in cardiomyocytes

Regulation of cardiac excitation–contraction coupling by action potential repolarization: role of the transient outward potassium current (Ito)

The cardiac action potential (AP) is critical for initiating and coordinating myocyte contraction. In particular, the early repolarization period of the AP (phase 1) strongly influences the time course and magnitude of the whole-cell intracellular Ca2+ transient by modulating trans-sarcolemmal Ca2+ influx through L-type Ca2+ channels (ICa,L) and Na-Ca exchangers (ICa,NCX). The transient outward potassium current (Ito) has kinetic properties that make it especially effective in modulating the trajectory of phase 1 repolarization and thereby cardiac excitation-contraction coupling (ECC). The magnitude of Ito varies greatly during cardiac development, between different regions of the heart, and is invariably reduced as a result of heart disease, leading to corresponding variations in ECC. In this article, we review evidence supporting a modulatory role of Ito in ECC through its influence on ICa,L, and possibly ICa,NCX. We also discuss differential effects of Ito on ECC between different species, between different regions of the heart and in heart disease