Xiong Ziman, Wu Peili, Zhang Yan, Chen Jun, Shen Yaqi, Kamel Ihab, Wu Bing, Zheng Xianying, Li Zhen
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China.
Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, Fujian, China.
Insights Imaging. 2024 Mar 13;15(1):70. doi: 10.1186/s13244-024-01640-9.
To achieve automated quantification of visceral adipose tissue (VAT) distribution in CT images and screen out parameters with discriminative value for inflammatory bowel disease (IBD) subtypes.
This retrospective multicenter study included Crohn's disease (CD) and ulcerative colitis (UC) patients from three institutions between 2012 and 2021, with patients with acute appendicitis as controls. An automatic VAT segmentation algorithm was developed using abdominal CT scans. The VAT volume, as well as the coefficient of variation (CV) of areas within the lumbar region, was calculated. Binary logistic regression and receiver operating characteristic analysis was performed to evaluate the potential of indicators to distinguish between IBD subtypes.
The study included 772 patients (365 CDs, median age [inter-quartile range] = 31.0. (25.0, 42.0) years, 255 males; 241 UCs, 46.0 (34.0, 55.5) years, 138 males; 166 controls, 40.0 (29.0, 53.0) years, 80 males). CD patients had lower VAT volume (CD = 1584.95 ± 1128.31 cm, UC = 1855.30 ± 1326.12 cm, controls = 2470.91 ± 1646.42 cm) but a higher CV (CD = 29.42 ± 15.54 %, p = 0.006 and p ˂ 0.001) compared to UC and controls (25.69 ± 12.61 % vs. 23.42 ± 15.62 %, p = 0.11). Multivariate analysis showed CV was a significant predictor for CD (odds ratio = 6.05 (1.17, 31.12), p = 0.03). The inclusion of CV improved diagnostic efficiency (AUC = 0.811 (0.774, 0.844) vs. 0.803 (0.766, 0.836), p = 0.08).
CT-based VAT distribution can serve as a potential biomarker for distinguishing IBD subtypes.
Visceral fat distribution features extracted from CT images using an automated segmentation algorithm (1.14 min) show differences between Crohn's disease and ulcerative colitis and are promising for practical radiological screening.
• Radiological parameters reflecting visceral fat distribution were extracted for the discrimination of Crohn's disease (CD) and ulcerative colitis (UC). • In CD, visceral fat was concentrated in the lower lumbar vertebrae, and the coefficient of variation was a significant predictor (OR = 6.05 (1.17, 31.12), p = 0.03). • The differences between CD, UC, and controls are promising for practical radiological screening.
实现CT图像中内脏脂肪组织(VAT)分布的自动量化,并筛选出对炎症性肠病(IBD)亚型具有鉴别价值的参数。
这项回顾性多中心研究纳入了2012年至2021年间来自三个机构的克罗恩病(CD)和溃疡性结肠炎(UC)患者,并以急性阑尾炎患者作为对照。利用腹部CT扫描开发了一种自动VAT分割算法。计算VAT体积以及腰椎区域内各区域的变异系数(CV)。进行二元逻辑回归和受试者工作特征分析,以评估各项指标区分IBD亚型的潜力。
该研究纳入了772例患者(365例CD患者,中位年龄[四分位间距]=31.0(25.0,42.0)岁,男性255例;241例UC患者,46.0(34.0,55.5)岁,男性138例;166例对照,40.0(29.0,53.0)岁,男性80例)。与UC患者和对照相比,CD患者的VAT体积较低(CD = 1584.95±1128.31 cm³,UC = 1855.30±1326.12 cm³,对照 = 2470.91±1646.42 cm³),但CV较高(CD = 29.42±15.54%,p = 0.006且p˂0.001)(UC为25.69±12.61%,对照为23.42±15.62%,p = 0.11)。多变量分析显示CV是CD的显著预测指标(比值比 = 6.05(1.17,31.12),p = 0.03)。纳入CV提高了诊断效率(曲线下面积 = 0.811(0.774,0.844)对0.803(0.766,0.836),p = 0.08)。
基于CT的VAT分布可作为区分IBD亚型的潜在生物标志物。
使用自动分割算法(1.14分钟)从CT图像中提取的内脏脂肪分布特征显示出克罗恩病和溃疡性结肠炎之间的差异,有望用于实际的放射学筛查。
• 提取反映内脏脂肪分布的放射学参数以鉴别克罗恩病(CD)和溃疡性结肠炎(UC)。• 在CD中,内脏脂肪集中在腰椎下部,变异系数是显著预测指标(比值比 = 6.05(1.17,31.12),p = 0.03)。• CD、UC和对照之间的差异有望用于实际的放射学筛查。