Computed tomography enterography-based deep learning radiomics to predict stratified healing in patients with Crohn’s disease: a multicenter study

Computed tomography enterography-based deep learning radiomics to predict stratified healing in patients with Crohn’s disease: a multicenter study

Abstract Objectives This study developed a deep learning radiomics (DLR) model utilizing baseline computed tomography enterography (CTE) to non-invasively predict stratified healing in Crohn’s disease (CD) patients following infliximab (IFX) treatment. Methods The study included 246 CD patients diag...

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Bibliographic Details
Main Authors: Chao Zhu, Kaicai Liu, Chang Rong, Chuanbin Wang, Xiaomin Zheng, Shuai Li, Shihui Wang, Jing Hu, Jianying Li, Xingwang Wu
Format: Article
Language:English
Published: SpringerOpen 2024-11-01
Series:Insights into Imaging
Subjects:
Crohn’s disease
Mucosal healing
Transmural healing
Radiomics
Deep learning
Online Access:https://doi.org/10.1186/s13244-024-01854-x
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https://doi.org/10.1186/s13244-024-01854-x

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