Cardiomyocyte-specific inactivation of thyroid hormone in pathologic ventricular hypertrophy: an adaptative response or part of the problem?

Recent studies in various rodent models of pathologic ventricular hypertrophy report the re-expression of deiodinase type 3 (D3) in cardiomyocytes. D3 inactivates thyroid hormone (T3) and is mainly expressed in tissues during development. The stimulation of D3 activity in ventricular hypertrophy and subsequent heart failure is associated with severe impairment of cardiac T3 signaling. Hypoxia-induced signaling appears to drive D3 expression in the hypertrophic cardiomyocyte, but other signaling cascades implicated in hypertrophy are also capable of stimulating transcription of the DIO3 gene. Many cardiac genes are transcriptionally regulated by T3 and impairment of T3 signaling will not only reduce energy turnover, but also lead to changes in gene expression that contribute to contractile dysfunction in pathologic remodeling. Whether stimulation of D3 activity and the ensuing local T3-deficiency is an adaptive response of the stressed heart or part of the pathologic signaling network leading to heart failure, remains to be established

Exploring the utility of radiomic feature extraction to improve the diagnostic accuracy of cardiac sarcoidosis using FDG PET

Background: This study aimed to explore the radiomic features from PET images to detect active cardiac sarcoidosis (CS). Methods: Forty sarcoid patients and twenty-nine controls were scanned using FDG PET-CMR. Five feature classes were compared between the groups. From the PET images alone, two different segmentations were drawn. For segmentation A, a region of interest (ROI) was manually delineated for the patients' myocardium hot regions with standardized uptake value (SUV) higher than 2.5 and the controls' normal myocardium region. A second ROI was drawn in the entire left ventricular myocardium for both study groups, segmentation B. The conventional metrics and radiomic features were then extracted for each ROI. Mann-Whitney U-test and a logistic regression classifier were used to compare the individual features of the study groups. Results: For segmentation A, the SUVmin had the highest area under the curve (AUC) and greatest accuracy among the conventional metrics. However, for both segmentations, the AUC and accuracy of the TBRmax were relatively high, >0.85. Twenty-two (from segmentation A) and thirty-five (from segmentation B) of 75 radiomic features fulfilled the criteria: P-value 0.5, and accuracy >0.7. Principal Component Analysis (PCA) was conducted, with five components leading to cumulative variance higher than 90%. Ten machine learning classifiers were then tested and trained. Most of them had AUCs and accuracies ≥0.8. For segmentation A, the AUCs and accuracies of all classifiers are >0.9, but k-neighbors and neural network classifiers were the highest (=1). For segmentation B, there are four classifiers with AUCs and accuracies ≥0.8. However, the gaussian process classifier indicated the highest AUC and accuracy (0.9 and 0.8, respectively). Conclusions: Radiomic analysis of the specific PET data was not proven to be necessary for the detection of CS. However, building an automated procedure will help to accelerate the analysis and potentially lead to more reproducible findings across different scanners and imaging centers and consequently improve standardization procedures that are important for clinical trials and development of more robust diagnostic protocols

PET-driven respiratory phase tracking and self-gating of PET data: clinical demonstration of enhanced lesion detectability in cardiovascular PET/MRI

The advent of cardiovascular PET/MR in clinic may offer superior motion compensation capabilities by exploiting the non-ionizing nature of MR to sufficiently track and model respiratory and cardiac motion at high spatial resolution. However, the estimated motion models need to be continuously driven by the respiratory and cardiac motion phase for the accurate motion correction of the PET and MR data, thereby demonstrating the need for constant cardio-respiratory phase tracking throughout the entire scan period. Although MR-based continuous monitoring of cardio-respiratory phase is possible, it could reserve valuable time from other diagnostic MR sequences, thus diminishing PET/MR clinical potential. In this study, we validate a readily applicable in clinic PET list-mode (LM) datadriven respiratory phase extraction method to enable robust respiratory self-gating of PET data. The method relies on the hypothesis that for certain tracers the total LM PET counts can be sensitive to the periodic movement of hot or cold activity regions in and out of the PET field of view during breathing. The respiratory phase is tracked by continuously monitoring the total LM counts temporal profile, after smoothing with moving average filters (MAFs) to automatically correct for deep breath hold and drifting patterns. Subsequently the phase is used to drive the self-gating of the PET data into five respiratory gates and a special breath-hold gate. The period of the respiratory phase extracted with our proposed method matched well with the expected period of normal human breathing. Moreover, the automatically identified irregular LM PET count patterns corresponded in time to breath-hold MR acquisitions. The clinical application of the proposed method on our cardiovascular PET/MR studies demonstrated feasibility, while quantitative assessment of 18F-NaF coronary lesions detectability suggested a 10% improvement in lesion contrast and contrast-tonoise scores for the self-gated PET images

Prognostic role of myocardial blood flow impairment in idiopathic left ventricular dysfunction

BACKGROUND: Depressed myocardial blood flow (MBF) has been reported in dilated cardiomyopathy. The aim of this study was to investigate whether MBF impairment is an independent predictor of prognosis in patients with idiopathic left ventricular (LV) dysfunction. METHODS AND RESULTS: Sixty-seven patients (52 male, mean age 52+/-12 years) with different degrees of idiopathic LV systolic dysfunction (average LV ejection fraction, 0.34+/-0.10; range, 0.07 to 0.49) were prospectively enrolled. Thirty-four subjects (51%) had no history of heart failure symptoms at enrollment (NYHA class I). All patients underwent clinical and functional evaluation and a PET study to measure absolute MBF at rest and after intravenous dipyridamole. During a mean follow-up of 45+/-37 months, 24 patients had major cardiac events, including cardiac death in 8 and development or progression of heart failure in 16 patients. Multivariate regression analysis (Cox proportional hazards model) revealed heart rate (chi(2) 11.06, P<0.001), LV end-diastolic dimension (chi(2) 11.73, P<0.001), and dipyridamole MBF (chi(2) 11.04, P<0.001) as independent predictors of subsequent cardiac events. Dipyridamole MBF < or = 1.36 mL. min(-1). g(-1) was associated with an increase in the relative risk of death, development, or progression of heart failure of 3.5 times over other more common clinical and functional variables. CONCLUSIONS: The present study demonstrates that severely depressed MBF is a predictor of poor prognosis in patients with idiopathic LV dysfunction independently of the degree of LV functional impairment and of the presence of overt heart failure