AI-Assisted MRE for Intestinal Fibrosis in Crohn's Disease
NCT ID: NCT06858553
Last Updated: 2025-08-05
Study Results
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Basic Information
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RECRUITING
234 participants
OBSERVATIONAL
2025-06-03
2027-02-28
Brief Summary
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Detailed Description
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Data Entry and Summary Process Management will enforce access regulations to ensure only authorized personnel can enter or query data. Patient information meeting inclusion criteria will be entered into a Tencent form from the hospital's medical record system. Research assistants will supplement this form with details about intestinal surgical specimens, including condition, quantity, and storage, and summarize all specimens. Researchers will summarize the MRE imaging data for the relevant patients. No one may delete, alter, copy, print, or output confidential data without management's consent.
Verification System During patient enrollment, information collection, and specimen collection, two or more research assistants or researchers will confirm the process. Relevant information will be verified again during specimen collection, labeling, and storage. In the analysis phase, researchers will recheck the accuracy of imaging, patient information, and specimens. Management will conduct a random audit every three months to verify patient inclusion criteria and confirm specimen information accuracy.
Data Dictionary A comprehensive data dictionary is used to define each variable collected within the registry. It includes the source of the variable, coding standards and any relevant normal ranges for clinical measures. This data dictionary serves as a reference to ensure uniformity in data collection and analysis.
Standard Operating Procedures (SOPs) The registry follows established Standard Operating Procedures (SOPs) for various registry functions, including patient recruitment, data collection, management, and analysis. SOPs also cover reporting procedures for adverse events, including guidelines for data reporting and event classification. Change management processes are in place to address any amendments or updates to registry protocols.
Sample Size Assessment A statistical sample size calculation has been performed to ensure that the registry is adequately powered to detect meaningful differences or effects. This calculation takes into account the expected incidence of the event of interest, anticipated variability, and the desired statistical power. The required number of participants or participant years is specified based on the primary and secondary objectives of the study.
Plan for Missing Data The registry has a clear policy for handling missing data, including cases where data may be unavailable, uninterpretable, or missing due to inconsistencies (e.g., out-of-range results). A specific protocol is followed for imputing missing values or excluding incomplete data from analysis, ensuring the final dataset remains reliable and valid for statistical analysis.
Statistical Analysis Methods Automatic recognition and segmentation of intestinal lesions in images, based on multi-parametric MRI data and artificial intelligence models, are used to evaluate intestinal fibrosis and assist in clinical decision-making. Specifically, the process includes: performing VOI annotation to generate 3D VOI; normalizing and resampling MRE images, cropping voxel intensity and applying min-max normalization; decomposing each 3D MRE lesion image into patches, and applying 5-fold data augmentation as input to the network; developing a deep learning segmentation algorithm using the nnU-Net model for automatic recognition of intestinal lesion images, with performance evaluated using the Dice similarity coefficient; constructing a ResNet model to accurately assess different degrees of intestinal fibrosis, with output as a predicted probability between 0 and 1; collecting multi-parametric MRI data prior to model construction and extracting features not affected by intestinal inflammation; excluding relevant features during model development, retaining only those reflecting intestinal fibrosis; after model construction, grouping patients based on inflammation severity and re-evaluating the AI model's recognition capability. Through these steps and the integration of multi-omics data, molecular subtyping and related prognostic analysis of patients are achieved to assist in clinical treatment decision-making.
Conditions
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Study Design
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COHORT
CROSS_SECTIONAL
Study Groups
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training group
This group of patients is used in the training phase of the predictive model to fit an appropriate model.
No interventions assigned to this group
validation group
This group of patients is used to validate the trained model to determine whether the model has broad applicability.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
2. Planning to receive a bowel resection due to stricture in ileum or colon, and availability of histological specimens of resected intestinal walls matched with MRE are expected to be available.
3. Clear boundaries of the target bowel tract enable accurate semi-automatic or fully automatic intestinal segmentation
Exclusion Criteria
2. Difficult to obtain suitable tissue after surgery
3. MRE imaging is of poor quality or contains artifacts
4. The target bowel is located at the anastomosis (ie, anastomotic stricture)
5. Intestinal lesions concurrent with other diseases
18 Years
75 Years
ALL
No
Sponsors
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Sixth Affiliated Hospital, Sun Yat-sen University
OTHER
Sir Run Run Shaw Hospital
OTHER
Jinling Hospital, China
OTHER
MSD R&D (China) Co., Ltd.
INDUSTRY
Ruijin Hospital
OTHER
Minhu Chen
OTHER
Responsible Party
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Minhu Chen
Professor
Principal Investigators
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Minhu Chen
Role: STUDY_CHAIR
First Affiliated Hospital, Sun Yat-Sen University
Locations
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The First Affiliated Hospital,Sun Yat-sen University
Guangzhou, Guangdong, China
Sixth Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China
Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University
Nanjing, Jiangsu, China
Ruijin Hospital, Shanghai Jiaotong University School of Medicine
Huangpu, Shanghai Municipality, China
Sir Run Run Shaw Hospital, Zhejiang University School of Medicine
Hangzhou, Zhejiang, China
Countries
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Central Contacts
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Facility Contacts
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References
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Li XH, Feng ST, Cao QH, Coffey JC, Baker ME, Huang L, Fang ZN, Qiu Y, Lu BL, Chen ZH, Li Y, Bettenworth D, Iacucci M, Sun CH, Ghosh S, Rieder F, Chen MH, Li ZP, Mao R. Degree of Creeping Fat Assessed by Computed Tomography Enterography is Associated with Intestinal Fibrotic Stricture in Patients with Crohn's Disease: A Potentially Novel Mesenteric Creeping Fat Index. J Crohns Colitis. 2021 Jul 5;15(7):1161-1173. doi: 10.1093/ecco-jcc/jjab005.
Li X, Zhang N, Hu C, Lin Y, Li J, Li Z, Cui E, Shi L, Zhuang X, Li J, Lu J, Wang Y, Liu R, Yuan C, Lin H, He J, Ke D, Tang S, Zou Y, He B, Sun C, Chen M, Huang B, Mao R, Feng ST. CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: A multicentre cohort study. EClinicalMedicine. 2022 Dec 30;56:101805. doi: 10.1016/j.eclinm.2022.101805. eCollection 2023 Feb.
Li Z, Chen Z, Zhang R, et, al. Eur J Nucl Med Mol Imaging. 2024 Feb 15.
Du JF, Lu BL, Huang SY, Mao R, Zhang ZW, Cao QH, Chen ZH, Li SY, Qin QL, Sun CH, Feng ST, Li ZP, Huang L, Li XH. A novel identification system combining diffusion kurtosis imaging with conventional magnetic resonance imaging to assess intestinal strictures in patients with Crohn's disease. Abdom Radiol (NY). 2021 Mar;46(3):936-947. doi: 10.1007/s00261-020-02765-3. Epub 2020 Sep 22.
Zhang MC, Li XH, Huang SY, Mao R, Fang ZN, Cao QH, Zhang ZW, Yan X, Chen MH, Li ZP, Sun CH, Feng ST. IVIM with fractional perfusion as a novel biomarker for detecting and grading intestinal fibrosis in Crohn's disease. Eur Radiol. 2019 Jun;29(6):3069-3078. doi: 10.1007/s00330-018-5848-6. Epub 2018 Dec 13.
Huang SY, Li XH, Huang L, Sun CH, Fang ZN, Zhang MC, Lin JJ, Jiang MJ, Mao R, Li ZP, Zhang Z, Feng ST. T2* Mapping to characterize intestinal fibrosis in crohn's disease. J Magn Reson Imaging. 2018 Apr 17. doi: 10.1002/jmri.26022. Online ahead of print.
Li Z, Lu B, Lin J, He S, Huang L, Wang Y, Meng J, Li Z, Feng ST, Lin S, Mao R, Li XH. A Type I Collagen-Targeted MR Imaging Probe for Staging Fibrosis in Crohn's Disease. Front Mol Biosci. 2021 Nov 11;8:762355. doi: 10.3389/fmolb.2021.762355. eCollection 2021.
Li XH, Mao R, Huang SY, Fang ZN, Lu BL, Lin JJ, Xiong SS, Chen MH, Li ZP, Sun CH, Feng ST. Ability of DWI to characterize bowel fibrosis depends on the degree of bowel inflammation. Eur Radiol. 2019 May;29(5):2465-2473. doi: 10.1007/s00330-018-5860-x. Epub 2019 Jan 11.
Chen YJ, Mao R, Li XH, Cao QH, Chen ZH, Liu BX, Chen SL, Chen BL, He Y, Zeng ZR, Ben-Horin S, Rimola J, Rieder F, Xie XY, Chen MH. Real-Time Shear Wave Ultrasound Elastography Differentiates Fibrotic from Inflammatory Strictures in Patients with Crohn's Disease. Inflamm Bowel Dis. 2018 Sep 15;24(10):2183-2190. doi: 10.1093/ibd/izy115.
Li XH, Mao R, Huang SY, Sun CH, Cao QH, Fang ZN, Zhang ZW, Huang L, Lin JJ, Chen YJ, Rimola J, Rieder F, Chen MH, Feng ST, Li ZP. Characterization of Degree of Intestinal Fibrosis in Patients with Crohn Disease by Using Magnetization Transfer MR Imaging. Radiology. 2018 May;287(2):494-503. doi: 10.1148/radiol.2017171221. Epub 2018 Jan 19.
Meng J, Luo Z, Chen Z, Zhou J, Chen Z, Lu B, Zhang M, Wang Y, Yuan C, Shen X, Huang Q, Zhang Z, Ye Z, Cao Q, Zhou Z, Xu Y, Mao R, Chen M, Sun C, Li Z, Feng ST, Meng X, Huang B, Li X. Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists. Eur Radiol. 2022 Dec;32(12):8692-8705. doi: 10.1007/s00330-022-08842-z. Epub 2022 May 26.
Li X, Liang D, Meng J, Zhou J, Chen Z, Huang S, Lu B, Qiu Y, Baker ME, Ye Z, Cao Q, Wang M, Yuan C, Chen Z, Feng S, Zhang Y, Iacucci M, Ghosh S, Rieder F, Sun C, Chen M, Li Z, Mao R, Huang B, Feng ST. Development and Validation of a Novel Computed-Tomography Enterography Radiomic Approach for Characterization of Intestinal Fibrosis in Crohn's Disease. Gastroenterology. 2021 Jun;160(7):2303-2316.e11. doi: 10.1053/j.gastro.2021.02.027. Epub 2021 Feb 17.
Provided Documents
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Document Type: Informed Consent Form
Other Identifiers
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MISP-102507
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
No.840[2024]
Identifier Type: -
Identifier Source: org_study_id
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