Zhang Han, Shen Yi, Cao Bo, Zheng Xiaomin, Zhao Dehan, Hu Jing, Wu Xingwang
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, People's Republic of China.
Department of Radiology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210011, People's Republic of China.
J Inflamm Res. 2025 Jan 6;18:183-194. doi: 10.2147/JIR.S491043. eCollection 2025.
Accurately assessing the activity of Crohn's disease (CD) is crucial for determining prognosis and guiding treatment strategies for CD patients.
This study aimed to develop and validate a nomogram for assessing CD activity.
The semi-automatic segmentation method and PyRadiomics software were employed to segment and extract radiomics features from the spectral CT enterography images of lesions in 107 CD patients. The radiomic score (rad-score) was calculated using the radiomic signature formula. Multivariate logistic regression analysis identified the independent risk factors of erythrocyte sedimentation rate, fecal calprotectin, and Inflammatory Bowel Disease Questionnaire (IBDQ), and a nomogram was constructed in combination with rad-score. The nomogram underwent evaluation and testing in the training set (n = 84) and validation set (n = 23), respectively.
The discrimination performance of the combined (AUC 0.877) was marginally superior to that of IBDQ + clinical (AUC 0.854). However, there was no significant difference in AUC between the two models in the validation set ( = 0.206). IBDQ + clinical outperformed clinical (AUC 0.808), clinical outperformed IBDQ (AUC 0.746), and IBDQ outperformed radiomic signature (AUC 0.688). Significant differences in AUC were observed between the two models (radiomic signature vs clinical, = 0.026; radiomic signature vs IBDQ + clinical, = 0.011; radiomic signature vs combined, = 0.008; in the validation set).
The nomogram, combined with laboratory data, IBDQ and rad-score, presents an accurate and reliable method for assessing CD activity.
The nomogram enhances the potential for personalized treatment plans and better disease management, making it a valuable tool for clinical practice.
准确评估克罗恩病(CD)的活动度对于确定CD患者的预后及指导治疗策略至关重要。
本研究旨在开发并验证一种用于评估CD活动度的列线图。
采用半自动分割方法及PyRadiomics软件,对107例CD患者病变的光谱CT小肠造影图像进行分割并提取影像组学特征。使用影像组学特征公式计算影像组学评分(rad-score)。多因素逻辑回归分析确定红细胞沉降率、粪便钙卫蛋白及炎症性肠病问卷(IBDQ)的独立危险因素,并结合rad-score构建列线图。该列线图分别在训练集(n = 84)和验证集(n = 23)中进行评估和测试。
联合模型(AUC 0.877)的鉴别性能略优于IBDQ + 临床模型(AUC 0.854)。然而,在验证集中,两个模型的AUC无显著差异( = 0.206)。IBDQ + 临床模型优于临床模型(AUC 0.808),临床模型优于IBDQ(AUC 0.746),IBDQ优于影像组学特征模型(AUC 0.688)。在验证集中,两个模型之间的AUC存在显著差异(影像组学特征模型与临床模型比较, = 0.026;影像组学特征模型与IBDQ + 临床模型比较, = 0.011;影像组学特征模型与联合模型比较, = 0.008)。
结合实验室数据、IBDQ及rad-score的列线图为评估CD活动度提供了一种准确可靠的方法。
该列线图增强了制定个性化治疗方案及改善疾病管理的可能性,使其成为临床实践中的一种有价值的工具。