We investigated the feasibility and advantages of using non-contrast CT
calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and
its association with major adverse cardiovascular events (MACE). PCAT features
from coronary CTA (CCTA) have been shown to be associated with cardiovascular
risk but are potentially confounded by iodine. If PCAT in CTCS images can be
similarly analyzed, it would avoid this issue and enable its inclusion in
formal risk assessment from readily available, low-cost CTCS images. To
identify coronaries in CTCS images that have subtle visual evidence of vessels,
we registered CTCS with paired CCTA images having coronary labels. We developed
a novel axial-disk method giving regions for analyzing PCAT features in three
main coronary arteries. We analyzed novel hand-crafted and radiomic features
using univariate and multivariate logistic regression prediction of MACE and
compared results against those from CCTA. Registration accuracy was sufficient
to enable the identification of PCAT regions in CTCS images. Motion or beam
hardening artifacts were often present in high-contrast CCTA but not CTCS. Mean
HU and volume were increased in both CTCS and CCTA for MACE group. There were
significant positive correlations between some CTCS and CCTA features,
suggesting that similar characteristics were obtained. Using
hand-crafted/radiomics from CTCS and CCTA, AUCs were 0.82/0.79 and 0.83/0.77
respectively, while Agatston gave AUC=0.73. Preliminarily, PCAT features can be
assessed from three main coronary arteries in non-contrast CTCS images with
performance characteristics that are at the very least comparable to CCTA.

通过分析非对比度 CTMS 图像，可以评估周冠状动脉脂肪组织（PCAT）并确定其与重大不良心血管事件（MACE）之间的关联。PCAT 特征可以在 CTCS 图像中定量分析，可用于低成本的心血管风险评估，而无需考虑碘造成的影响。

CT 钙化评分图像中周围冠状动脉脂肪组织特征分析与冠状动脉 CTA 的比较

Pericoronary adipose tissue feature analysis in CT calcium score images  with comparison to coronary CTA

Vascular calcification is implicated as an important factor in major adverse
cardiovascular events (MACE), including heart attack and stroke. A controversy
remains over how to integrate the diverse forms of vascular calcification into
clinical risk assessment tools. Even the commonly used calcium score for
coronary arteries, which assumes risk scales positively with total
calcification, has important inconsistencies. Fundamental studies are needed to
determine how risk is influenced by the diverse calcification phenotypes.
However, studies of these kinds are hindered by the lack of high-throughput,
objective, and non-destructive tools for classifying calcification in imaging
data sets. Here, we introduce a new classification system for phenotyping
calcification along with a semi-automated, non-destructive pipeline that can
distinguish these phenotypes in even atherosclerotic tissues. The pipeline
includes a deep-learning-based framework for segmenting lipid pools in noisy
micro-CT images and an unsupervised clustering framework for categorizing
calcification based on size, clustering, and topology. This approach is
illustrated for five vascular specimens, providing phenotyping for thousands of
calcification particles across as many as 3200 images in less than seven hours.
Average Dice Similarity Coefficients of 0.96 and 0.87 could be achieved for
tissue and lipid pool, respectively, with training and validation needed on
only 13 images despite the high heterogeneity in these tissues. By introducing
an efficient and comprehensive approach to phenotyping calcification, this work
enables large-scale studies to identify a more reliable indicator of the risk
of cardiovascular events, a leading cause of global mortality and morbidity.

血管钙化是心脏病发作和中风等重大不良心血管事件的重要因素。本研究提出了一种用于测定钙化表型的新的分类系统，以及一种半自动、无损的钙化分类工具，能够在动脉粥样硬化组织中区分这些表型。通过引入高效全面的钙化表型分类方法，本研究使得大规模研究能够识别心血管事件的更可靠风险指标。

利用人工智能对血管组织中的钙化进行表型化

Phenotyping calcification in vascular tissues using artificial  intelligence

In this work, we propose a multi-task recurrent neural network with attention
mechanism for predicting cardiovascular events from electronic health records
(EHRs) at different time horizons. The proposed approach is compared to a
standard clinical risk predictor (QRISK) and machine learning alternatives
using 5-year data from a NHS Foundation Trust. The proposed model outperforms
standard clinical risk scores in predicting stroke (AUC=0.85) and myocardial
infarction (AUC=0.89), considering the largest time horizon. Benefit of using
an \gls{mt} setting becomes visible for very short time horizons, which results
in an AUC increase between 2-6%. Further, we explored the importance of
individual features and attention weights in predicting cardiovascular events.
Our results indicate that the recurrent neural network approach benefits from
the hospital longitudinal information and demonstrates how machine learning
techniques can be applied to secondary care.

本研究提出了一种基于多任务循环神经网络和注意机制的方法，用于预测心血管事件。与标准的临床风险评估模型相比，该模型在预测中风和心肌梗死方面具有更好的性能，并且在短期内的预测效果可以通过使用多任务学习得到提升。同时，该研究还探讨了个体特征和注意力权重对于预测心血管事件的重要性，并证明了机器学习技术可以被应用于二级保健。