Academic radiology · 2025
Machine Learning Models Based on CT Enterography for Differentiating Between Ulcerative Colitis and Colonic Crohn's Disease Using Intestinal Wall, Mesenteric Fat, and Visceral Fat Features.
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Wang Xia, Wang Xingwei, Lei Jie, Rong Chang, Zheng Xiaomin, Li Shuai, Gao Yankun, Wu Xingwang
Аннотация
PURPOSE: This study aimed to develop radiomic-based machine learning models using computed tomography enterography (CTE) features derived from the intestinal wall, mesenteric fat, and visceral fat to differentiate between ulcerative colitis (UC) and colonic Crohn's disease (CD).
METHODS: Clinical and imaging data from 116 patients with inflammatory bowel disease (IBD) (68 with UC and 48 with colonic CD) were retrospectively collected. Radiomic features were extracted from venous-phase CTE images. Feature selection was performed via the intraclass correlation coefficient (ICC), correlation analysis, SelectKBest, and least absolute shrinkage and selection operator (LASSO) regression. Support vector machine models were constructed using features from individual and combined regions, with model performance evaluated using the area under the ROC curve (AUC).
RESULTS: The combined radiomic model, integrating features from all three regions, exhibited superior classification performance (AUC= 0.857, 95% CI, 0.732-0.982), with a sensitivity of 0.762 (95% CI, 0.547-0.903) and specificity of 0.857 (95% CI, 0.601-0.960) in the testing cohort. The models based on features from the intestinal wall, mesenteric fat, and visceral fat achieved AUCs of 0.847 (95% CI, 0.710-0.984), 0.707 (95% CI, 0.526-0.889), and 0.731 (95% CI, 0.553-0.910), respectively, in the testing cohort. The intestinal wall model demonstrated the best calibration.
CONCLUSION: This study demonstrated the feasibility of constructing machine learning models based on radiomic features of the intestinal wall, mesenteric fat, and visceral fat to distinguish between UC and colonic CD.
METHODS: Clinical and imaging data from 116 patients with inflammatory bowel disease (IBD) (68 with UC and 48 with colonic CD) were retrospectively collected. Radiomic features were extracted from venous-phase CTE images. Feature selection was performed via the intraclass correlation coefficient (ICC), correlation analysis, SelectKBest, and least absolute shrinkage and selection operator (LASSO) regression. Support vector machine models were constructed using features from individual and combined regions, with model performance evaluated using the area under the ROC curve (AUC).
RESULTS: The combined radiomic model, integrating features from all three regions, exhibited superior classification performance (AUC= 0.857, 95% CI, 0.732-0.982), with a sensitivity of 0.762 (95% CI, 0.547-0.903) and specificity of 0.857 (95% CI, 0.601-0.960) in the testing cohort. The models based on features from the intestinal wall, mesenteric fat, and visceral fat achieved AUCs of 0.847 (95% CI, 0.710-0.984), 0.707 (95% CI, 0.526-0.889), and 0.731 (95% CI, 0.553-0.910), respectively, in the testing cohort. The intestinal wall model demonstrated the best calibration.
CONCLUSION: This study demonstrated the feasibility of constructing machine learning models based on radiomic features of the intestinal wall, mesenteric fat, and visceral fat to distinguish between UC and colonic CD.