Evaluation of Mucosal Healing in Crohn’s Disease: Radiomics Models of Intestinal Wall and Mesenteric Fat Based on Dual-Energy CT

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
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Abstract

This study aims to assess the effectiveness of radiomics signatures obtained from dual-energy computed tomography enterography (DECTE) in the evaluation of mucosal healing (MH) in patients diagnosed with Crohn’s disease (CD). In this study, 106 CD patients with a total of 221 diseased intestinal segments (79 with MH and 142 non-MH) from two medical centers were included and randomly divided into training and testing cohorts at a ratio of 7:3. Radiomics features were extracted from the enteric phase iodine maps and 40-kev and 70-kev virtual monoenergetic images (VMIs) of the diseased intestinal segments, as well as from mesenteric fat. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) logistic regression. Radiomics models were subsequently established, and the accuracy of these models in identifying MH in CD was assessed by calculating the area under the receiver operating characteristic curve (AUC). The combined-iodine model formulated by integrating the intestinal and mesenteric fat radiomics features of iodine maps exhibited the most favorable performance in evaluating MH, with AUCs of 0.989 (95% confidence interval (CI) 0.977–1.000) in the training cohort and 0.947 (95% CI 0.884–1.000) in the testing cohort. Patients categorized as high risk by the combined-iodine model displayed a greater probability of experiencing disease progression when contrasted with low-risk patients. The combined-iodine radiomics model, which is built upon iodine maps of diseased intestinal segments and mesenteric fat, has demonstrated promising performance in evaluating MH in CD patients.

评估克罗恩病的黏膜愈合:基于双能量 CT 的肠壁和肠系膜脂肪放射组学模型
摘要 本研究旨在评估从双能计算机断层扫描肠造影(DECTE)中获得的放射组学特征在评估克罗恩病(CD)患者粘膜愈合(MH)中的有效性。这项研究纳入了来自两个医疗中心的106名克罗恩病患者,共221个病变肠段(79个MH肠段和142个非MH肠段),并按7:3的比例随机分为训练组和测试组。从病变肠段的肠相碘图、40-kev 和 70-kev 虚拟单能图像(VMI)以及肠系膜脂肪中提取放射组学特征。特征选择采用最小绝对收缩和选择算子(LASSO)逻辑回归法。随后建立了放射组学模型,并通过计算接收者操作特征曲线下面积(AUC)评估了这些模型在鉴别 CD 中 MH 的准确性。通过整合碘图的肠道和肠系膜脂肪放射组学特征而建立的联合碘模型在评估MH方面表现最出色,训练队列中的AUC为0.989(95%置信区间(CI)0.977-1.000),测试队列中的AUC为0.947(95%置信区间(CI)0.884-1.000)。与低风险患者相比,被联合碘模型归类为高风险的患者出现疾病进展的概率更高。联合碘放射组学模型建立在病变肠段和肠系膜脂肪的碘图基础上,在评估 CD 患者的 MH 方面表现出了良好的性能。
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来源期刊
Journal of Digital Imaging
Journal of Digital Imaging 医学-核医学
CiteScore
7.50
自引率
6.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.
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