A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.

IF 2.2 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Tomography Pub Date : 2024-09-03 DOI:10.3390/tomography10090108

Arshpreet Singh Badesha, Russell Frood, Marc A Bailey, Patrick M Coughlin, Andrew F Scarsbrook

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引用次数: 0

Abstract

Background: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities. This scoping review aims to summarise the existing literature on radiomic analysis techniques in cardiovascular disease.

Methods: MEDLINE and Embase databases were searched for eligible studies evaluating radiomic techniques in living human subjects derived from CT, MRI or PET imaging investigating atherosclerotic disease. Data on study population, imaging characteristics and radiomics methodology were extracted.

Results: Twenty-nine studies consisting of 5753 patients (3752 males) were identified, and 78.7% of patients were from coronary artery studies. Twenty-seven studies employed CT imaging (19 CT carotid angiography and 6 CT coronary angiography (CTCA)), and two studies studied PET/CT. Manual segmentation was most frequently undertaken. Processing techniques included voxel discretisation, voxel resampling and filtration. Various shape, first-order, second-order and higher-order radiomic features were extracted. Logistic regression was most commonly used for machine learning.

Conclusion: Most published evidence was feasibility/proof of concept work. There was significant heterogeneity in image acquisition, segmentation techniques, processing and analysis between studies. There is a need for the implementation of standardised imaging acquisition protocols, adherence to published reporting guidelines and economic evaluation.

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对 CT 和 PET 成像进行机器学习衍生辐射组学分析以调查动脉粥样硬化性心血管疾病的范围综述。

背景：心血管疾病主要影响颈动脉、冠状动脉、主动脉和外周动脉。放射组学涉及从肉眼无法感知的成像特征中提取定量数据。心血管疾病的放射组学分析主要集中在 CT 和 MRI 模式上。本综述旨在总结有关心血管疾病放射组学分析技术的现有文献：方法：在 MEDLINE 和 Embase 数据库中检索了符合条件的研究，这些研究评估了活体人体 CT、MRI 或 PET 成像调查动脉粥样硬化疾病的放射学技术。提取了有关研究人群、成像特征和放射组学方法的数据：结果：共确定了 29 项研究，包括 5753 名患者（3752 名男性），其中 78.7% 的患者来自冠状动脉研究。27项研究采用了CT成像技术（19项CT颈动脉造影术和6项CT冠状动脉造影术（CTCA）），2项研究采用了PET/CT技术。人工分割是最常用的方法。处理技术包括体素离散化、体素重采样和过滤。提取了各种形状、一阶、二阶和高阶放射学特征。逻辑回归最常用于机器学习：大多数已发表的证据都是可行性/概念验证工作。不同研究在图像采集、分割技术、处理和分析方面存在很大差异。有必要实施标准化的成像采集协议，遵守已发布的报告指南和经济评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Tomography Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

2.70

自引率

10.50%

发文量

222

期刊介绍： TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.