Zhang Jingwen, Qin Shanyu, Jiang Haixing
The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Jpn J Radiol. 2025 Apr 11. doi: 10.1007/s11604-025-01779-5.
To establish and validate a model based on CT imaging during follow-ups for predicting the disease progression in ileal stricturing Crohn's disease (CD).
Between January 2014 and February 2024, a retrospective review was conducted on 71 patients (training, n = 49; test, n = 22) who were initially diagnosed with ileal stricturing CD. Disease progression referred to the development of penetrating diseases, the requirement for CD-related hospitalization or surgery during follow-up. Radiomics features were extracted from visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) on baseline and follow-up CT scans, respectively. Integrating clinical characteristics and body composition features, a novel CT-based delta-radiomics nomogram was established according to multivariate Cox stepwise regression analysis. Receiver operating characteristic (ROC) analysis was performed to assess diagnostic performance.
The delta-VAT radiomics model (RM) exhibited satisfactory performance in training cohort (the area under the ROC curve [AUC] = 0.792, 95% confidence Interval [CI] 0.666-0.917) and in test cohort (AUC = 0.640, 95% CI 0.411-0.870). The AUCs of the delta-SAT RM were 0.777 (95% CI 0.648-0.907) in training cohort and 0.612 (95% CI 0.377-0.846) in test cohort. The combined nomogram model showed good discrimination for predicting disease progression, with a C-index of 0.808 and 0.702 in the training and test cohorts, respectively.
We first constructed a comprehensive model incorporating delta-adipose radiomics, baseline neutrophil-to-lymphocyte ratio (NLR) level and the application of biological therapy to predict progression in ileal stricturing CD, which aids in the timely adjustment of therapeutic strategies and enhances patients' quality of life.
建立并验证一种基于随访期间CT成像的模型，用于预测回肠狭窄型克罗恩病（CD）的疾病进展。
2014年1月至2024年2月，对71例最初诊断为回肠狭窄型CD的患者进行回顾性研究（训练组，n = 49；测试组，n = 22）。疾病进展是指随访期间穿透性疾病的发生、因CD相关原因住院或手术。分别从基线和随访CT扫描中提取内脏脂肪组织（VAT）和皮下脂肪组织（SAT）的影像组学特征。结合临床特征和身体成分特征，根据多变量Cox逐步回归分析建立了一种基于CT的新型增量影像组学列线图。采用受试者操作特征（ROC）分析评估诊断性能。
增量VAT影像组学模型（RM）在训练队列（ROC曲线下面积[AUC] = 0.792，95%置信区间[CI] 0.666 - 0.917）和测试队列（AUC = 0.640，95% CI 0.411 - 0.870）中表现出令人满意的性能。增量SAT RM在训练队列中的AUC为0.777（95% CI 0.648 - 0.907），在测试队列中的AUC为0.612（95% CI 0.377 - 0.846）。联合列线图模型在预测疾病进展方面显示出良好的区分能力，训练队列和测试队列的C指数分别为0.808和0.702。
我们首次构建了一个综合模型，纳入增量脂肪影像组学、基线中性粒细胞与淋巴细胞比值（NLR）水平和生物治疗的应用，以预测回肠狭窄型CD的进展，这有助于及时调整治疗策略并提高患者生活质量。
