Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn's disease activity

作者全名：Li, Junlin; Xie, Gang; Tang, Wuli; Zhang, Lingqin; Zhang, Yue; Zhang, Lingfeng; Wang, Danni; Li, Kang

作者地址：[Li, Junlin; Zhang, Lingfeng; Li, Kang] North Sichuan Med Coll, Nanchong 637100, Peoples R China; [Li, Junlin; Tang, Wuli; Zhang, Lingqin; Zhang, Yue; Zhang, Lingfeng; Wang, Danni; Li, Kang] Chongqing Gen Hosp, Dept Radiol, Chongqing 401121, Peoples R China; [Xie, Gang] Chengdu Third Peoples Hosp, Dept Radiol, Chengdu 610031, Peoples R China; [Tang, Wuli; Zhang, Yue; Li, Kang] Chongqing Med Univ, Chongqing 400016, Peoples R China

通信作者：Li, K (通讯作者)，North Sichuan Med Coll, Nanchong 637100, Peoples R China.; Li, K (通讯作者)，Chongqing Gen Hosp, Dept Radiol, Chongqing 401121, Peoples R China.; Li, K (通讯作者)，Chongqing Med Univ, Chongqing 400016, Peoples R China.

来源：INSIGHTS INTO IMAGING

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001220779200003

JCR分区：Q1

影响因子：4.1

年份：2024

卷号：15

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Inflammatory bowel disease; Crohn's disease; Dual energy CT; Machine learning; Activity

摘要：Objectives The simplified endoscopic score of Crohn's disease (SES-CD) is the gold standard for quantitatively evaluating Crohn's disease (CD) activity but is invasive. This study aimed to develop and validate a machine learning (ML) model based on dual-energy CT enterography (DECTE) to noninvasively evaluate CD activity. Methods We evaluated the activity in 202 bowel segments of 46 CD patients according to the SES-CD score and divided the segments randomly into training set and testing set at a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) was used for feature selection, and three models based on significant parameters were established based on logistic regression. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and clinical decision curves. Results There were 110 active and 92 inactive bowel segments. In univariate analysis, the slope of spectral curve in the venous phases (lambda HU-V) has the best diagnostic performance, with an area under the ROC curve (AUC) of 0.81 and an optimal threshold of 1.975. In the testing set, the AUC of the three models established by the 7 variables to differentiate CD activity was 0.81-0.87 (DeLong test p value was 0.071-0.766, p > 0.05), and the combined model had the highest AUC of 0.87 (95% confidence interval (CI): 0.779-0.959). Conclusions The ML model based the DECTE can feasibly evaluate CD activity, and DECTE parameters provide a quantitative analysis basis for evaluating specific bowel activities in CD patients. Critical relevance statement The machine learning model based on dual-energy computed tomography enterography can be used for evaluating Crohn's disease activity noninvasively and quantitatively.

基金机构：Chongqing's technological innovation and application development [cstc2020jscx-sbqwX0015]

基金资助正文：This work was supported by the Chongqing's technological innovation and application development (cstc2020jscx-sbqwX0015).