ArticleEClinicalMedicine2023
CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: A multicentre cohort study.
Article in EClinicalMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06858553 (A Prospective, Multi-center Study to Characterize Intestinal Fibrosis in Patients With Crohn's Disease), which is not on this map. Cited by 31 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Prospective, Multi-center Study to Characterize Intestinal Fibrosis in Patients With Crohn's Disease (CD) Using MR Enterography (MRE)-Based Artificial Intelligence
Who cites it
31 citing papers in PubMed, 43 citations in OpenAlex.
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- Predicting the progressive-free survival of ALK-positive lung cancer: machine learning algorithm based on habitat imaging.Journal of thoracic disease · 2025Article
- CT-based delta-radiomics signature of visceral adipose tissue for prediction of disease progression in ileal stricturing Crohn's disease.Japanese journal of radiology · 2025Article
- Heart failure and obesity: Translational approaches and therapeutic perspectives. A scientific statement of the Heart Failure Association of the ESC.European journal of heart failure · 2025Article
- Longitudinal computed tomography-based delta-radiomics of visceral adipose tissue predicts infliximab secondary loss of response in Crohn's disease patients.World journal of gastroenterology · 2025Article
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Authors and funding
25 authors at 9 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, Interpretation: Our results suggest that VAT is an important determinant of disease progression in patients with CD. Our VAT-RM allows the accurate identification of high-risk patients prone to disease progression and offers notable advantages over SAT-RM. Funding: This study was supported by the National Natural Science Foundation of China, Guangdong Basic and Applied Basic Research Foundation, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Nature Science Foundation of Shenzhen, and Young S&T Talent Training Program of Guangdong Provincial Association for S&T. Translation: For the Chinese translation of the abstract see Supplementary Materials section.
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