心外膜脂肪组织放射组学在胸部计算机断层扫描诊断冠状动脉慢血流的价值。

IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Jing Tong, Libo Zhang, Guiguang Bei, Wenyuan Liu, Mingyu Zou, Yuze Li, Xiaogang Li, Yu Sun, Xinrui Wang, Jingya Zhu, Zhenguo Wang, Benqiang Yang
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引用次数: 0

摘要

背景:目前,冠状动脉慢流(CSF)的诊断依赖于冠状动脉造影,无创影像学检查对CSF的诊断研究尚不充分。本研究旨在探讨心外膜脂肪组织(EAT)放射组学在胸部计算机断层扫描(CT)中诊断脑脊液的价值。方法:回顾性研究211例冠状动脉造影显示冠状动脉狭窄的患者。结果:保留16个放射组学特征,建立诊断脑脊液的EAT放射组学模型。该模型在训练集中诊断脑脊液的AUC为0.81,灵敏度为0.72,特异性为0.79,准确性为0.76;在验证集中诊断脑脊液的AUC为0.77,灵敏度为0.82,特异性为0.71,准确性为0.77。校正曲线显示模型与实际结果具有良好的一致性,决策分析曲线显示模型在最合理的阈值概率范围内具有良好的整体净效益。结论:基于胸部CT的EAT放射组学模型对脑脊液有较好的诊断效果,有望成为一种有潜力的无创脑脊液诊断工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The value of diagnosing coronary slow flow based on epicardial adipose tissue radiomics in chest computed tomography.

Background: At present, the diagnosis of coronary slow flow (CSF) relies on coronary angiography, and non-invasive imaging examinations for the diagnosis of CSF have not been fully studied. This study aimed to explore the value of diagnosing CSF based on epicardial adipose tissue (EAT) radiomics in chest computed tomography (CT).

Methods: This retrospective study included 211 patients who underwent coronary angiography showing coronary artery stenosis < 40% from January 2020 to December 2021 and underwent chest CT within 2 weeks before angiography. According to the thrombolysis in myocardial infarction flow grade, the patients were divided into CSF group (n = 103) and normal coronary flow group (n = 108). Establish an automatic method for segmenting EAT on chest CT images. Patients were randomly divided into a training set (n = 148) and a validation set (n = 63) at a ratio of 7:3, and then radiomics features were extracted. Features selected using the maximum relevance minimum redundancy and the least absolute shrinkage and selection operator were adopted to construct an EAT radiomics model. The diagnostic efficacy of the model for CSF was evaluated using the area under the receiver operating characteristic curve. The consistency between the model and the actual results was evaluated using calibration curves, and the clinical application value of the model was evaluated using decision curve analysis.

Results: 16 radiomics features were retained to establish an EAT radiomics model for diagnosing CSF. The model had an AUC of 0.81, sensitivity of 0.72, specificity of 0.79, and accuracy of 0.76 for diagnosing CSF in the training set, and an AUC of 0.77, sensitivity of 0.82, specificity of 0.71, and accuracy of 0.77 in the validation set. The calibration curves showed good consistency between the model and the actual results, while the decision analysis curves showed good overall net benefits of the model within most reasonable threshold probability ranges.

Conclusions: The EAT radiomics model based on chest CT had good diagnostic efficacy for CSF and may become a potential non-invasive tool for diagnosing CSF.

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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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