Zhang Bo, Gao Yankun, Tong Li, 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 Third Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230061, People's Republic of China.
J Inflamm Res. 2025 Aug 8;18:10681-10694. doi: 10.2147/JIR.S526700. eCollection 2025.
This study sought to develop and validate a radiomics nomogram using computed tomography enterography (CTE) to predict clinical adverse outcomes (CAO) in patients with stricturing Crohn's disease (CD), aiding in personalized treatment planning.
We retrospectively collected data from 219 patients diagnosed with stricturing CD between January 2018 and March 2023 at our institution, dividing them into a training set (n=153) and a testing set (n=66). Radiomics features from strictured segments were extracted and the most predictive features were identified using Pearson correlation, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to derive a Radiomics score (Rad-score). Cox regression was used to select key clinical predictors of CAO. A radiomics nomogram was developed to predict CAO, evaluated using Harrell's concordance index (C-index), time-dependent Receiver Operating Characteristic (ROC) curves, and Decision Curve Analysis (DCA).
Univariate and multivariate Cox regression analyses of the training set identified the HBI score (HR=0.443, 95% CI=0.212-0.925, =0.030) and the diameter of the upstream lumen (HR=1.080, 95% CI=1.050-1.111, <0.001) as independent clinical predictors of CAO in stricturing CD. Nineteen features related to CAO outcomes were selected for Rad-score calculation. In the testing set, the C-index for the clinical, radiomics, and nomogram models were 0.752, 0.775, and 0.849, respectively. The AUCs of the nomogram model at 1, 2, and 3 years were 0.874, 0.863, and 0.956, respectively.
The CTE-based radiomics nomogram significantly outperformed clinical and radiomics models alone and demonstrated excellent predictive accuracy for CAO risk. By integrating the HBI score and upstream lumen diameter with radiomics features, this tool provides clinicians with a validated, noninvasive method to stratify stricturing CD patients by risk and guide personalized therapeutic decisions.
本研究旨在开发并验证一种基于计算机断层扫描小肠造影（CTE）的影像组学列线图，以预测狭窄性克罗恩病（CD）患者的临床不良结局（CAO），辅助制定个性化治疗方案。
我们回顾性收集了2018年1月至2023年3月在我院诊断为狭窄性CD的219例患者的数据，将其分为训练集（n = 153）和测试集（n = 66）。提取狭窄节段的影像组学特征，并使用Pearson相关性分析、SelectKBest算法以及最小绝对收缩和选择算子（LASSO）回归确定最具预测性的特征，以得出影像组学评分（Rad-score）。采用Cox回归选择CAO的关键临床预测因素。开发了一种影像组学列线图来预测CAO，并使用Harrell一致性指数（C-index）、时间依赖性受试者操作特征（ROC）曲线和决策曲线分析（DCA）进行评估。
训练集的单因素和多因素Cox回归分析确定，HBI评分（HR = 0.443，95%CI = 0.212 - 0.925，P = 0.030）和上游肠腔直径（HR = 1.080，95%CI = 1.050 - 1.111，P < 0.001）是狭窄性CD患者CAO的独立临床预测因素。选择了19个与CAO结局相关的特征用于计算Rad-score。在测试集中，临床模型、影像组学模型和列线图模型的C-index分别为0.752、0.775和0.849。列线图模型在1年、2年和3年时的AUC分别为0.874、0.863和0.956。
基于CTE的影像组学列线图显著优于单独的临床模型和影像组学模型，对CAO风险具有出色的预测准确性。通过将HBI评分和上游肠腔直径与影像组学特征相结合该工具为临床医生提供了一种经过验证的非侵入性方法，可根据风险对狭窄性CD患者进行分层，并指导个性化治疗决策。
