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Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease

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To investigate whether volumetric visceral adipose tissue (VAT) features extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approach are effective in differentiating Crohn’s disease (CD) and ulcerative colitis (UC).


This retrospective study enrolled 316 patients (mean age, 36.25 ± 13.58 [standard deviation]; 219 men) with confirmed diagnosis of CD and UC who underwent CT enterography between 2012 and 2021. Volumetric VAT was semi-automatically segmented on the arterial phase images. Radiomics analysis was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. We developed a 3D-CNN model using VAT imaging data from the training cohort. Clinical covariates including age, sex, modified body mass index, and disease duration that impact VAT were added to the machine learning model for adjustment. The model’s performance was evaluated on the testing cohort separating from the model’s development process by its discrimination and clinical utility.


Volumetric VAT radiomics analysis with LASSO had the highest AUC value of 0.717 (95% CI, 0.614–0.820), though difference of diagnostic performance among the 3D-CNN model (AUC = 0.693; 95% CI, 0.587–0.798) and radiomics analysis with PCA (AUC = 0.662; 95% CI, 0.548–0.776) and LASSO have not reached statistical significance (all p > 0.05). The radiomics score was higher in UC than in CD on the testing cohort (mean ± SD, UC 0.29 ± 1.05 versus CD −0.60 ± 1.25; p < 0.001). The LASSO model with adjustment of clinical covariates reached an AUC of 0.775 (95%CI, 0.683–0.868).


The developed volumetric VAT–based radiomics and 3D-CNN models provided comparable and effective performance for the characterization of CD from UC.

Key Points

• High-output feature data extracted from volumetric visceral adipose tissue on CT enterography had an effective diagnostic performance for differentiating Crohn’s disease from ulcerative colitis.

• With adjustment of clinical covariates that cause difference in volumetric visceral adipose tissue, adjusted clinical machine learning model reached stronger performance when distinguishing Crohn’s disease patients from ulcerative colitis patients.

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Fig. 1
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Three-dimensional convolutional neural network


The area under the receiver operating characteristic curves


Crohn’s disease


CT enterography


Inflammatory bowel disease


The least absolute shrinkage and selection operator


Principal component analysis


The receiver operating curve


Ulcerative colitis


Visceral adipose tissue


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We thank Xinyueyuan Hao and Fangqin Tan for the segmentation of the visceral adipose tissue. The authors thank Yuhan Tang from the School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, China, for advice on statistical analysis.


This work is supported by grants from the National Natural Science Foundation of China (NSFC), Nos. 82071890 and 82071889.

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Correspondence to Peng Xiao or Yaqi Shen.

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The scientific guarantor of this publication is Yaqi Shen.

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Statistics and biometry

Two of the authors have significant statistical expertise.

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Zhou, Z., Xiong, Z., Cheng, R. et al. Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease. Eur Radiol 33, 1862–1872 (2023).

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