克罗恩病的影像组学特征与英夫利昔单抗继发反应丧失之间的相关性。

Correlation between radiomic features of Crohn's disease and secondary loss of response to infliximab.

作者信息

Li Shuai, Zhu Chao, Tong Li, Zheng Xiao-Min, Rong Chang, Gao Yan-Kun, Yuan Dong-Cun, Wu Xing-Wang

机构信息

Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230031, Anhui Province, China.

Department of Radiology, Yijishan Hospital of Wanan Medical University, Wuhu 241000, Anhui Province, China.

出版信息

World J Gastroenterol. 2025 Jul 21;31(27):109459. doi: 10.3748/wjg.v31.i27.109459.

DOI:10.3748/wjg.v31.i27.109459

PMID:40741098

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12305135/

Abstract

BACKGROUND

Crohn's disease (CD) is a type of inflammatory bowel disease, with chronic and progressive characteristics. Infliximab (IFX) can rapidly relieve CD-related symptoms and promote mucosal healing. However, some patients may occur secondary loss of response (SLOR) during the maintenance treatment, leading to the recurrence or progression of CD. The current IFX efficacy prediction models for CD have limited applicability to SLOR. Radiomics, as a non-invasive technique, is expected to serve as a more accurate tool for predicting the risk of SLOR.

AIM

To develop a radiomics-based model via integrative analysis of intestinal wall and creeping fat to predict SLOR in CD.

METHODS

We retrospectively analyzed clinical and imaging data from 220 CD patients in two centers. Univariate and multivariate analyses were used to screen out clinically independent predictors of SLOR. Radiomics features of the intestinal wall and creeping fat were extracted and fused together for analysis. Univariate and least absolute shrinkage and selection operator analyses were used to select the most valuable radiomics features to calculate Radscore and develop radiomics predictive model. A combined predictive model was developed based on the Radscore and clinically independent predictors through multivariate logistic regression analysis. Area under the receiver operating characteristic curve (AUC), calibration curve and the decision curve analysis were used to verify model performance.

RESULTS

White blood cell count, disease duration and Harvey-Bradshaw Index were identified as clinically independent predictors of SLOR to develop the clinical model. Fifteen most valuable radiomics features were selected to develop the radiomics model. Compared with the clinical and radiomics models, the combined model achieved the best prediction performance, with AUCs were 0.871 (95%CI: 0.814-0.929) in the training cohort and 0.854 (95%CI: 0.759-0.949) in the validation cohort.

CONCLUSION

The combined model that integrates intestinal wall and creeping fat analysis is valuable for predicting the SLOR of IFX in CD.

摘要

背景

克罗恩病（CD）是一种炎症性肠病，具有慢性和进行性特征。英夫利昔单抗（IFX）可迅速缓解CD相关症状并促进黏膜愈合。然而，部分患者在维持治疗期间可能出现继发性失应答（SLOR），导致CD复发或进展。目前用于CD的IFX疗效预测模型对SLOR的适用性有限。放射组学作为一种非侵入性技术，有望成为预测SLOR风险的更准确工具。

目的

通过对肠壁和爬行脂肪进行综合分析，建立基于放射组学的模型以预测CD患者的SLOR。

方法

我们回顾性分析了两个中心220例CD患者的临床和影像数据。采用单因素和多因素分析筛选出SLOR的临床独立预测因素。提取肠壁和爬行脂肪的放射组学特征并融合分析。采用单因素分析和最小绝对收缩和选择算子分析选择最有价值的放射组学特征来计算Radscore并建立放射组学预测模型。通过多因素逻辑回归分析，基于Radscore和临床独立预测因素建立联合预测模型。采用受试者操作特征曲线（AUC）下面积、校准曲线和决策曲线分析来验证模型性能。

结果

白细胞计数、病程和哈维-布拉德肖指数被确定为SLOR的临床独立预测因素，用于建立临床模型。选择15个最有价值的放射组学特征建立放射组学模型。与临床模型和放射组学模型相比，联合模型的预测性能最佳，在训练队列中的AUC为0.871（95%CI：0.814-0.929）．在验证队列中的AUC为0.854（95%CI：0.759-0.９49）。