Fluorine-18 fluorodeoxyglucose positron emission tomography-based textural features for prediction of event prone carotid atherosclerotic plaques

Radiomics in Carotid Plaque: A Systematic Review and Radiomics Quality Score Assessment

Imaging modalities provide information on plaque morphology and vulnerability; however, they are operator dependent and miss a great deal of microscopic information. Recently, many radiomics models for carotid plaque that identify unstable plaques and predict cardiovascular outcomes have been proposed. This systematic review was aimed at assessing whether radiomics is a reliable and reproducible method for the clinical prediction of carotid plaque. A systematic search was conducted to identify studies published in PubMed and Cochrane library from January 1, 2001, to September 30, 2022. Both retrospective and prospective studies that developed and/or validated machine learning models based on radiomics data to classify or predict carotid plaques were included. The general characteristics of each included study were selected, and the methodological quality of radiomics reports and risk of bias were evaluated using the radiomics quality score (RQS) tool and Quality Assessment of Diagnostic Accuracy Studies-2, respectively. Two investigators independently reviewed each study, and the consensus data were used for analysis. A total of 2429 patients from 16 studies were included. The mean area under the curve of radiomics models for diagnostic or predictive performance of the included studies was 0.88 ± 0.02, with a range of 0.741-0.989. The mean RQS was 9.25 (standard deviation: 6.04), representing 25.7% of the possible maximum value of 36, whereas the lowest point was -2, and the highest score was 22. Radiomics models have revealed additional information on patients with carotid plaque, but with respect to methodological quality, radiomics reports are still in their infancy, and many hurdles need to be overcome.

2022 Artificial intelligence primer for the nuclear cardiologist

Driven by advances in computing power, the past decade has seen rapid developments in artificial intelligence (AI) which now offers potential enhancements to every aspect of nuclear cardiology workflow including acquisition, reconstruction, segmentation, direct image analysis, and interpretation; as well as facilitating clinical and imaging big-data integration for superior personalized risk stratification. To understand the relevance and potential of AI in their field, this review provides a primer for nuclear cardiologists in 2022. The aim is to explain terminology and provide a summary of key current implementations, challenges, and future aspirations of AI-based enhancements to nuclear cardiology.

Gestational week-specific of uterine artery Doppler indices in predicting preeclampsia: a hospital-based retrospective cohort study

BACKGROUND

Plenty of studies explored the relationship between uterine artery (UtA) Doppler indices and the onset of preeclampsia at different trimesters. However, few studies test the gestational week-specific predictive value of the UtA indices for subsequent preeclampsia and compare the difference of right or left UtA indices (e.g., pulsatility or resistance index [PI or RI]).

METHODS

Hospital-based retrospective cohort study of singleton pregnant women who received the Doppler test between 2012 and 2016 was conducted in 2018. The predictive performance of the UtA indices for preeclampsia and its variants, including early-onset preeclampsia (< 34 weeks) and preterm preeclampsia (< 37 weeks), was estimated.

RESULTS

The UtA indices, with a cutoff value of 1.11 for the right and left UtA-PI, and 0.66 and 0.63 for the right and left UtA-RI, respectively, were effective predictors for subsequent preeclampsia. The prediction was satisfactory at the 9th week of the Doppler scan: areas under the curve ≥ 0.80, the Youden index ranging from 0.54 to 0.58, the sensitivity of 63.2 ~ 73.7%, and the specificity 84.2 ~ 91.3%, respectively. The UtA indices had comparable performance in screening for early-onset and preterm preeclampsia but showed lower predictive value for late-onset cases. Among these indices, the right UtA-RI had the highest specificity (all P < 0.01), while the left UtA-PI showed good authenticity (higher Youden index) in predicting the disorder.

CONCLUSIONS

The second-trimester measured UtA indices had a satisfactory performance at the 9th week in predicting subsequent preeclampsia. The right UtA-RI was the first choice in ruling out preeclampsia, while the left UtA-PI showed the best authenticity of the prediction.

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Serum Ceramides as Prognostic Biomarkers of Large Thrombus Burden in Patients with STEMI: A Micro-Computed Tomography Study

ST-elevation myocardial infarction (STEMI) remains one of the leading causes of mortality worldwide. The identification of novel metabolic and imaging biomarkers could unveil key pathophysiological mechanisms at the molecular level and promote personalized care in patients with acute coronary syndromes. We studied 38 patients with STEMI who underwent primary percutaneous coronary intervention and thrombus aspiration. We sought to correlate serum ceramide levels with micro-CT quantified aspirated thrombus volume and relevant angiographic outcomes, including modified TIMI thrombus grade and pre- or post-procedural TIMI flow. Higher ceramide C16:0 levels were significantly but weakly correlated with larger aspirated thrombus volume (Spearman r = 0.326, p = 0.046), larger intracoronary thrombus burden (TB; p = 0.030) and worse pre- and post-procedural TIMI flow (p = 0.049 and p = 0.039, respectively). Ceramides C24:0 and C24:1 were also significantly associated with larger intracoronary TB (p = 0.008 and p = 0.001, respectively). Receiver operating characteristic analysis demonstrated that ceramides C24:0 and C24:1 could significantly predict higher intracoronary TB (area under the curve: 0.788, 95% CI: 0.629-0.946 and 0.846, 95% CI: 0.706-0.985, respectively). In conclusion, serum ceramide levels were higher among patients with larger intracoronary and aspirated TB. This suggests that quantification of serum ceramides might improve risk-stratification of patients with STEMI and facilitate an individualized approach in clinical practice.

Role of 18F-FDG positron emission tomography in carotid atherosclerotic plaque imaging: A systematic review

Stroke and other thromboembolic events in the brain are often due to carotid artery atherosclerosis, and atherosclerotic plaques with inflammation are considered particularly vulnerable, with an increased risk of becoming symptomatic. Positron emission tomography (PET) with 2-deoxy-2-[Fluorine-18] fluoro-D-glucose (¹⁸F-FDG) provides valuable metabolic information regarding arteriosclerotic lesions and may be applied for the detection of vulnerable plaque. At present, however, patients are selected for carotid surgical intervention on the basis of the degree of stenosis alone, and not the vulnerability or inflammation of the lesion. During the past decade, research using PET with the glucose analog tracer ¹⁸F-fluor-deoxy-glucose, has been implemented for identifying increased tracer uptake in symptomatic carotid plaques, and tracer uptake has been shown to correlate with plaque inflammation and vulnerability. These findings imply that ¹⁸F-FDG PET might hold the promise for a new and better diagnostic test to identify patients eligible for carotid endarterectomy. The rationale for developing diagnostic tests based on molecular imaging with ¹⁸F-FDG PET, as well as methods for simple clinical PET approaches, are discussed. This is a systematic review, following Preferred Reporting Items for Systematic Reviews guidelines, which interrogated the PUBMED database from January 2001 to November 2019. The search combined the terms, “atherosclerosis,” “inflammation,” “FDG,” and “plaque imaging.” The search criteria included all types of studies, with a primary outcome of the degree of arterial vascular inflammation determined by ¹⁸F-FDG uptake. This review examines the role of ¹⁸F-FDG PET imaging in the characterization of atherosclerotic plaques.