Suppr超能文献

一种新型计算机断层肠摄影放射组学方法用于克罗恩病肠纤维化特征分析的开发和验证。

Development and Validation of a Novel Computed-Tomography Enterography Radiomic Approach for Characterization of Intestinal Fibrosis in Crohn's Disease.

机构信息

Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.

Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, People's Republic of China.

出版信息

Gastroenterology. 2021 Jun;160(7):2303-2316.e11. doi: 10.1053/j.gastro.2021.02.027. Epub 2021 Feb 17.

Abstract

BACKGROUND & AIMS: No reliable method for evaluating intestinal fibrosis in Crohn's disease (CD) exists; therefore, we developed a computed-tomography enterography (CTE)-based radiomic model (RM) for characterizing intestinal fibrosis in CD.

METHODS

This retrospective multicenter study included 167 CD patients with 212 bowel lesions (training, 98 lesions; test, 114 lesions) who underwent preoperative CTE and bowel resection at 1 of the 3 tertiary referral centers from January 2014 through June 2020. Bowel fibrosis was histologically classified as none-mild or moderate-severe. In the training cohort, 1454 radiomic features were extracted from venous-phase CTE and a machine learning-based RM was developed based on the reproducible features using logistic regression. The RM was validated in an independent external test cohort recruited from 3 centers. The diagnostic performance of RM was compared with 2 radiologists' visual interpretation of CTE using receiver operating characteristic (ROC) curve analysis.

RESULTS

In the training cohort, the area under the ROC curve (AUC) of RM for distinguishing moderate-severe from none-mild intestinal fibrosis was 0.888 (95% confidence interval [CI], 0.818-0.957). In the test cohort, the RM showed robust performance across 3 centers with an AUC of 0.816 (95% CI, 0.706-0.926), 0.724 (95% CI, 0.526-0.923), and 0.750 (95% CI, 0.560-0.940), respectively. Moreover, the RM was more accurate than visual interpretations by either radiologist (radiologist 1, AUC = 0.554; radiologist 2, AUC = 0.598; both, P < .001) in the test cohort. Decision curve analysis showed that the RM provided a better net benefit to predicting intestinal fibrosis than the radiologists.

CONCLUSIONS

A CTE-based RM allows for accurate characterization of intestinal fibrosis in CD.

摘要

背景与目的

目前尚无可靠的方法来评估克罗恩病(CD)中的肠纤维化;因此,我们开发了一种基于计算机断层肠摄影术(CTE)的放射组学模型(RM)来对 CD 中的肠纤维化进行特征描述。

方法

这项回顾性多中心研究纳入了 2014 年 1 月至 2020 年 6 月期间在 3 家三级转诊中心中的 1 家接受术前 CTE 和肠道切除术的 167 例 CD 患者,共 212 处肠道病变(训练队列 98 处病变;测试队列 114 处病变)。肠道纤维化的组织学分类为无-轻度或中-重度。在训练队列中,从静脉期 CTE 中提取了 1454 个放射组学特征,并使用逻辑回归基于可重复的特征开发了基于机器学习的 RM。该 RM 在从 3 个中心招募的独立外部测试队列中进行了验证。通过受试者工作特征(ROC)曲线分析比较了 RM 与 2 位放射科医生对 CTE 的视觉解读的诊断性能。

结果

在训练队列中,用于区分中-重度与无-轻度肠道纤维化的 RM 的 ROC 曲线下面积(AUC)为 0.888(95%置信区间[CI],0.818-0.957)。在测试队列中,RM 在 3 个中心均表现出良好的性能,AUC 分别为 0.816(95%CI,0.706-0.926)、0.724(95%CI,0.526-0.923)和 0.750(95%CI,0.560-0.940)。此外,在测试队列中,RM 比任何一位放射科医生的视觉解读都更准确(放射科医生 1,AUC=0.554;放射科医生 2,AUC=0.598;均 P<.001)。决策曲线分析表明,与放射科医生相比,RM 对预测肠道纤维化具有更好的净收益。

结论

基于 CTE 的 RM 可准确描述 CD 中的肠纤维化。

相似文献

引用本文的文献

10
Artificial intelligence in inflammatory bowel disease.炎症性肠病中的人工智能
Saudi J Gastroenterol. 2025 Jul 1;31(4):197-205. doi: 10.4103/sjg.sjg_46_25. Epub 2025 Apr 25.

本文引用的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验