CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: a multicentre cohort studyResearch in context

Background: Visceral adipose tissue (VAT) is involved in the pathogenesis of Crohn's disease (CD). However, data describing its effects on CD progression remain scarce. We developed and validated a VAT-radiomics model (RM) using computed tomography (CT) images to predict disease progression in...

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Published in:EClinicalMedicine 2023-02, Vol.56, p.101805
Main Authors: Xuehua Li, Naiwen Zhang, Cicong Hu, Yuqin Lin, Jiaqiang Li, Zhoulei Li, Enming Cui, Li Shi, Xiaozhao Zhuang, Jianpeng Li, Jiahang Lu, Yangdi Wang, Renyi Liu, Chenglang Yuan, Haiwei Lin, Jinshen He, Dongping Ke, Shanshan Tang, Yujian Zou, Bo He, Canhui Sun, Minhu Chen, Bingsheng Huang, Ren Mao, Shi-Ting Feng
Format: Article
Language:English
Subjects:
Computed tomography enterography
Crohn's disease
Radiomics
Visceral adipose tissue
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Summary:Background: Visceral adipose tissue (VAT) is involved in the pathogenesis of Crohn's disease (CD). However, data describing its effects on CD progression remain scarce. We developed and validated a VAT-radiomics model (RM) using computed tomography (CT) images to predict disease progression in patients with CD and compared it with a subcutaneous adipose tissue (SAT)-RM. Methods: This retrospective study included 256 patients with CD (training, n = 156; test, n = 100) who underwent baseline CT examinations from June 19, 2015 to June 14, 2020 at three tertiary referral centres (The First Affiliated Hospital of Sun Yat-Sen University, The First Affiliated Hospital of Shantou University Medical College, and The First People's Hospital of Foshan City) in China. Disease progression referred to the development of penetrating or stricturing diseases or the requirement for CD-related surgeries during follow-up. A total of 1130 radiomics features were extracted from VAT on CT in the training cohort, and a machine-learning–based VAT-RM was developed to predict disease progression using selected reproducible features and validated in an external test cohort. Using the same modeling methodology, a SAT-RM was developed and compared with the VAT-RM. Findings: The VAT-RM exhibited satisfactory performance for predicting disease progression in total test cohort (the area under the ROC curve [AUC] = 0.850, 95% confidence Interval [CI] 0.764–0.913, P 
ISSN:2589-5370
2589-5370