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Myocardial fibrosis and inflammation by CMR predict cardiovascular outcome in people living with HIV
(2021)
Objectives_: The goal of this study was to examine prognostic relationships between cardiac imaging measures and cardiovascular outcome in people living with human immunodeficiency virus (HIV) (PLWH) on highly active antiretroviral therapy (HAART).
Background: PLWH have a higher prevalence of cardiovascular disease and heart failure (HF) compared with the noninfected population. The pathophysiological drivers of myocardial dysfunction and worse cardiovascular outcome in HIV remain poorly understood.
Methods: This prospective observational longitudinal study included consecutive PLWH on long-term HAART undergoing cardiac magnetic resonance (CMR) examination for assessment of myocardial volumes and function, T1 and T2 mapping, perfusion, and scar. Time-to-event analysis was performed from the index CMR examination to the first single event per patient. The primary endpoint was an adjudicated adverse cardiovascular event (cardiovascular mortality, nonfatal acute coronary syndrome, an appropriate device discharge, or a documented HF hospitalization).
Results: A total of 156 participants (62% male; age [median, interquartile range]: 50 years [42 to 57 years]) were included. During a median follow-up of 13 months (9 to 19 months), 24 events were observed (4 HF deaths, 1 sudden cardiac death, 2 nonfatal acute myocardial infarction, 1 appropriate device discharge, and 16 HF hospitalizations). Patients with events had higher native T1 (median [interquartile range]: 1,149 ms [1,115 to 1,163 ms] vs. 1,110 ms [1,075 to 1,138 ms]); native T2 (40 ms [38 to 41 ms] vs. 37 ms [36 to 39 ms]); left ventricular (LV) mass index (65 g/m2 [49 to 77 g/m2] vs. 57 g/m2 [49 to 64 g/m2]), and N-terminal pro–B-type natriuretic peptide (109 pg/l [25 to 337 pg/l] vs. 48 pg/l [23 to 82 pg/l]) (all p < 0.05). In multivariable analyses, native T1 was independently predictive of adverse events (chi-square test, 15.9; p < 0.001; native T1 [10 ms] hazard ratio [95% confidence interval]: 1.20 [1.08 to 1.33]; p = 0.001), followed by a model that also included LV mass (chi-square test, 17.1; p < 0.001). Traditional cardiovascular risk scores were not predictive of the adverse events.
Conclusions: Our findings reveal important prognostic associations of diffuse myocardial fibrosis and LV remodeling in PLWH. These results may support development of personalized approaches to screening and early intervention to reduce the burden of HF in PLWH (International T1 Multicenter Outcome Study; NCT03749343).
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Background: Computed tomography (CT) low-dose (LD) imaging is used to lower radiation exposure, especially in vascular imaging; in current literature, this is mostly on latest generation high-end CT systems.
Purpose: To evaluate the effects of reduced tube current on objective and subjective image quality of a 15-year-old 16-slice CT system for pulmonary angiography (CTPA).
Material and Methods: CTPA scans from 60 prospectively randomized patients (28 men, 32 women) were examined in this study on a 15-year-old 16-slice CT scanner system. Standard CT (SD) settings were 100 kV and 150 mAs, LD settings were 100 kV and 50 mAs. Attenuation of the pulmonary trunk, various anatomic landmarks, and image noise were quantitatively measured; contrast-to-noise ratios (CNR) and signal-to-noise ratios (SNR) were calculated. Three independent blinded radiologists subjectively rated each image series using a 5-point grading scale.
Results: CT dose index (CTDI) in the LD series was 66.46% lower compared to the SD settings (2.49 ± 0.55 mGy versus 7.42 ± 1.17 mGy). Attenuation of the pulmonary trunk showed similar results for both series (SD 409.55 ± 91.04 HU; LD 380.43 HU ± 93.11 HU; P = 0.768). Subjective image analysis showed no significant differences between SD and LD settings regarding the suitability for detection of central and peripheral PE (central SD/LD, 4.88; intra-class correlation coefficients [ICC], 0.894/4.83; ICC, 0.745; peripheral SD/LD, 4.70; ICC, 0.943/4.57; ICC, 0.919; all P > 0.4).
Conclusion: The LD protocol, on a 15-year-old CT scanner system without current high-end hardware or post-processing tools, led to a dose reduction of approximately 67% with similar subjective image quality and delineation of central and peripheral pulmonary arteries.