Zeng Junxiang, Huai Manxiu, Ge Wensong, Yang Zhigang, Pan Xiujun
Department of Clinical Laboratory, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China.
Department of Gastroenterology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Arab J Gastroenterol. 2025 Feb;26(1):45-52. doi: 10.1016/j.ajg.2024.05.003. Epub 2024 Jul 27.
Currently, an increasing amount of experimental data is available on newly discovered biomarkers in inflammatory bowel diseases (IBD), but the role of these biomarkers is often questionable due to their limited sensitivity. Therefore, this study aimed to build a diagnostic tool incorporating a panel of serum biomarkers into a computational algorithm to identify patients with IBD and differentiate those with Crohn's disease (CD) from those with ulcerative colitis (UC).
We studied sera from 192 CD patients, 118 UC patients, 60 non-IBD controls and 60 healthy controls. Indirect immunofluorescence (IIF) assays were utilized to determine several serum biomarkers previously associated with IBD, and the decision tree algorithm was used to construct the diagnosis model. Performances of models were evaluated by prediction accuracy, precision, AUC and Matthews's correlation coefficient (MCC). The "Inflammatory Bowel Disease Multi-omics Database (IBDMDB)" cohorts were used to validate the model as external validation set.
The prediction rates were determined and compared for decision tree models after each data was developed using C5.0, C&RT, QUEST and CHAID. The C5.0 and CHAID algorithms, which ranked top for the prediction rate in the IBD vs. non-IBD model and the CD vs. UC model, respectively, were utilized for final pattern analysis. The final decision tree model achieved higher classification accuracy than the approach based on conservative marker combinations (sensitivity 75.0% vs. 79.5%, specificity 93.8% vs. 78.3% for differentiating IBD from non-IBD; and sensitivity 84.3% vs. 73.4%, specificity 92.5% vs. 54.9% for differentiating CD from UC, respectively). The model prediction consistency was 93% (28/30) in the external validation set.
The decision-tree-based approach used in this study, based on serum biomarkers, has shown to be a valid and useful approach to identifying IBD and differentiating CD from UC.
目前，关于炎症性肠病（IBD）中新发现的生物标志物已有越来越多的实验数据，但由于其敏感性有限，这些生物标志物的作用常常受到质疑。因此，本研究旨在构建一种诊断工具，将一组血清生物标志物纳入计算算法，以识别IBD患者，并区分克罗恩病（CD）患者和溃疡性结肠炎（UC）患者。
我们研究了192例CD患者、118例UC患者、60例非IBD对照者和60例健康对照者的血清。采用间接免疫荧光（IIF）测定法来确定几种先前与IBD相关的血清生物标志物，并使用决策树算法构建诊断模型。通过预测准确性、精确性、AUC和马修斯相关系数（MCC）评估模型的性能。使用“炎症性肠病多组学数据库（IBDMDB）”队列作为外部验证集来验证该模型。
在使用C5.0、C&RT、QUEST和CHAID对每个数据进行开发后，确定并比较了决策树模型的预测率。分别在IBD与非IBD模型以及CD与UC模型中预测率排名第一的C5.0和CHAID算法被用于最终的模式分析。最终的决策树模型比基于保守标志物组合的方法具有更高的分类准确性（区分IBD与非IBD时，敏感性分别为75.0%对79.5%，特异性分别为93.8%对78.3%；区分CD与UC时，敏感性分别为84.3%对73.4%，特异性分别为92.5%对54.9%）。该模型在外部验证集中的预测一致性为93%（28/30）。
本研究中基于血清生物标志物的基于决策树的方法已证明是识别IBD以及区分CD与UC的一种有效且有用的方法。
