使用基于计算机断层扫描小肠造影的放射组学和血清标志物预测克罗恩病活动度

Prediction Crohn's Disease Activity Using Computed Tomography Enterography-Based Radiomics and Serum Markers.

作者信息

Wang Peipei, Liu Yu, Wang Yuanjun

机构信息

School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, 200093, Shanghai, China (P.W., Y.W.).

Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No. 639, Zhizaoju Road, Huangpu District, Shanghai 200011, China (Y.L.).

出版信息

Acad Radiol. 2025 Jun 30. doi: 10.1016/j.acra.2025.06.009.

DOI:10.1016/j.acra.2025.06.009

PMID:40592645

Abstract

RATIONALE AND OBJECTIVES

Accurate stratification of the activity index of Crohn's disease (CD) using computed tomography enterography (CTE) radiomics and serum markers can aid in predicting disease progression and assist physicians in personalizing therapeutic regimens for patients with CD.

MATERIALS AND METHODS

This retrospective study enrolled 233 patients diagnosed with CD between January 2019 and August 2024. Patients were divided into training and testing cohorts at a ratio of 7:3 and further categorized into remission, mild active phase, and moderate-severe active phase groups based on simple endoscopic score for CD (SEC-CD). Radiomics features were extracted from CTE venous images, and T-test and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. The serum markers were selected based on the variance analysis. We also developed a random forest (RF) model for multi-class stratification of CD. The model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and quantified the contribution of each feature in the dataset to CD activity via Shapley additive exPlanations (SHAP) values. Finally, we enrolled gender, radiomics scores, and serum scores to develop a nomogram model to verify the effectiveness of feature extraction.

RESULTS

14 non-zero coefficient radiomics features and six serum markers with significant differences (P<0.01) were ultimately selected to predict CD activity. The AUC (micro/macro) for the ensemble machine learning model combining the radiomics features and serum markers is 0.931/0.928 for three-class. The AUC for the remission phase, the mild active phase, and the moderate-severe active phase were 0.983, 0.852, and 0.917, respectively. The mean AUC for the nomogram model was 0.940.

CONCLUSION

A radiomics model was developed by integrating radiomics and serum markers of CD patients, achieving enhanced consistency with SEC-CD in grade CD. This model has the potential to assist clinicians in accurate diagnosis and treatment.

摘要

原理与目的

使用计算机断层扫描小肠造影（CTE）影像组学和血清标志物对克罗恩病（CD）的活动指数进行准确分层，有助于预测疾病进展，并协助医生为CD患者制定个性化治疗方案。

材料与方法

这项回顾性研究纳入了2019年1月至2024年8月期间诊断为CD的233例患者。患者按7:3的比例分为训练组和测试组，并根据CD的简单内镜评分（SEC-CD）进一步分为缓解期、轻度活动期和中度-重度活动期组。从CTE静脉期图像中提取影像组学特征，并应用T检验和最小绝对收缩和选择算子（LASSO）回归进行特征选择。血清标志物基于方差分析进行选择。我们还开发了一个随机森林（RF）模型用于CD的多分类分层。通过受试者操作特征曲线下面积（AUC）评估模型性能，并通过Shapley值加法解释（SHAP）值量化数据集中每个特征对CD活动的贡献。最后，纳入性别、影像组学评分和血清评分以建立列线图模型，验证特征提取的有效性。

结果

最终选择了14个非零系数的影像组学特征和6个有显著差异（P<0.01）的血清标志物来预测CD活动。结合影像组学特征和血清标志物的集成机器学习模型在三类分层中的AUC（微观/宏观）为0.931/0.928。缓解期、轻度活动期和中度-重度活动期的AUC分别为0.983、0.852和0.917。列线图模型的平均AUC为0.940。