Identification of neural alterations in patients with Crohn's disease with a novel multiparametric brain MRI-based radiomics model

被引:0
|
作者
Zhang, Ruo-nan [1 ]
Wang, Yang-di [1 ]
Wang, Hai-jie [2 ]
Ke, Yao-qi [1 ]
Shen, Xiao-di [1 ]
Huang, Li [1 ]
Lin, Jin-jiang [1 ]
He, Wei-tao [1 ]
Zhao, Chen [3 ]
Li, Zhou-lei [1 ]
Mao, Ren [4 ]
Wang, Ye-jun [5 ,6 ]
Yang, Guang [2 ]
Li, Xue-hua [1 ]
机构
[1] Sun Yat Sen Univ, Affiliated Hosp 1, Dept Radiol, Guangzhou 510080, Peoples R China
[2] East China Normal Univ, Shanghai Key Lab Magnet Resonance, Dongchuan Rd, Shanghai 200241, Peoples R China
[3] Siemens Healthineers, MR Res Collaborat Team, Guangzhou, Peoples R China
[4] Sun Yat sen Univ, Affiliated Hosp 1, Dept Gastroenterol, Guangzhou 510080, Peoples R China
[5] Shenzhen Univ, Youth Innovat Team Med Bioinformat, Med Sch, Shenzhen 518060, Peoples R China
[6] Shenzhen Univ, Coll Basic Med, Dept Cell Biol & Genet, Med Sch, Shenzhen 518060, Peoples R China
来源
INSIGHTS INTO IMAGING | 2024年 / 15卷 / 01期
基金
中国国家自然科学基金;
关键词
Crohn's disease; Brain MRI; Gut-brain axis; Radiomics; Multiomics; INFLAMMATORY-BOWEL-DISEASE; ANTIDEPRESSANTS;
D O I
10.1186/s13244-024-01859-6
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
ObjectivesGut-brain axis dysfunction has emerged as a key contributor to the pathogenesis of Crohn's disease (CD). The elucidation of neural alterations may provide novel insights into its management. We aimed to develop a multiparameter brain MRI-based radiomics model (RM) for characterizing neural alterations in CD patients and to interpret these alterations using multiomics traits.MethodsThis prospective study enrolled 230 CD patients and 46 healthy controls (HCs). Participants voluntarily underwent brain MRI and psychological assessment (n = 155), blood metabolomics analysis (n = 260), and/or fecal 16S rRNA sequencing (n = 182). The RM was developed using 13 features selected from 13,870 first-order features extracted from multiparameter brain MRI in training cohort (CD, n = 75; HCs, n = 32) and validated in test cohort (CD, n = 34; HCs, n = 14). Multiomics data (including gut microbiomics, blood metabolomics, and brain radiomics) were compared between CD patients and HCs.ResultsIn the training cohort, area under the receiver operating characteristic curve (AUC) of RM for distinguishing CD patients from HCs was 0.991 (95% confidence interval (CI), 0.975-1.000). In test cohort, RM showed an AUC of 0.956 (95% CI, 0.881-1.000). CD-enriched blood metabolites such as triacylglycerol (TAG) exhibited significant correlations with both brain features detected by RM and CD-enriched microbiota (e.g., Veillonella). One notable correlation was found between Veillonella and Ctx-Lh-Middle-Temporal-CBF-p90 (r = 0.41). Mediation analysis further revealed that dysbiosis, such as of Veillonella, may regulate the blood flow in the middle temporal cortex through TAG.ConclusionWe developed a multiparameter MRI-based RM that characterized the neural alterations of CD patients, and multiomics data offer potential evidence to support the validity of our model. Our study may offer clues to help provide potential therapeutic targets.Critical relevance statementOur brain-gut axis study developed a novel model using multiparameter MRI and radiomics to characterize brain changes in patients with Crohn's disease. We validated this model's effectiveness using multiomics data, making it a potential biomarker for better patient management.Key PointsUtilizing multiparametric MRI and radiomics techniques could unveil Crohn's disease's neurophenotype.The neurophenotype radiomics model is interpreted using multiomics data.This model may serve as a novel biomarker for Crohn's disease management.
引用
收藏
页数:20
相关论文
共 50 条
  • [1] Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer
    Haoxin Zheng
    Qi Miao
    Yongkai Liu
    Sohrab Afshari Mirak
    Melina Hosseiny
    Fabien Scalzo
    Steven S. Raman
    Kyunghyun Sung
    European Radiology, 2022, 32 : 5688 - 5699
  • [2] Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer
    Zheng, Haoxin
    Miao, Qi
    Liu, Yongkai
    Mirak, Sohrab Afshari
    Hosseiny, Melina
    Scalzo, Fabien
    Raman, Steven S.
    Sung, Kyunghyun
    EUROPEAN RADIOLOGY, 2022, 32 (08) : 5688 - 5699
  • [3] A Novel MRI-Based Radiomics Model for Predicting Recurrence in Chordoma
    Wei, Wei
    Wang, Ke
    Tian, Kaibing
    Liu, Zhenyu
    Wang, Liang
    Zhang, Junting
    Tang, Zhenchao
    Wang, Shuo
    Dong, Di
    Zang, Yali
    Gao, Yuan
    Wu, Zhen
    Tian, Jie
    2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2018, : 139 - 142
  • [4] Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer
    Wang, Hui
    Chen, Xiaoyong
    Ding, Jingfeng
    Deng, Shuitang
    Mao, Guoqun
    Tian, Shuyuan
    Zhu, Xiandi
    Ao, Weiqun
    ABDOMINAL RADIOLOGY, 2023, 48 (02) : 471 - 485
  • [5] Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer
    Hui Wang
    Xiaoyong Chen
    Jingfeng Ding
    Shuitang Deng
    Guoqun Mao
    Shuyuan Tian
    Xiandi Zhu
    Weiqun Ao
    Abdominal Radiology, 2023, 48 : 471 - 485
  • [6] A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer
    Xu, Kepei
    Hua, Meiqi
    Mai, Ting
    Ren, Xiaojing
    Fang, Xiaozheng
    Wang, Chunjie
    Ge, Min
    Qian, Hua
    Xu, Maosheng
    Zhang, Ruixin
    JOURNAL OF IMAGING INFORMATICS IN MEDICINE, 2024, 37 (04): : 1475 - 1487
  • [7] A Multiparametric MRI-Based Radiomics Nomogram for Preoperative Prediction of Survival Stratification in Glioblastoma Patients With Standard Treatment
    Jia, Xin
    Zhai, Yixuan
    Song, Dixiang
    Wang, Yiming
    Wei, Shuxin
    Yang, Fengdong
    Wei, Xinting
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [8] Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer
    Cheng, Yuan
    Luo, Yahong
    Hu, Yue
    Zhang, Zhaohe
    Wang, Xingling
    Yu, Qing
    Liu, Guanyu
    Cui, Enuo
    Yu, Tao
    Jiang, Xiran
    ABDOMINAL RADIOLOGY, 2021, 46 (11) : 5072 - 5085
  • [9] Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer
    Yuan Cheng
    Yahong Luo
    Yue Hu
    Zhaohe Zhang
    Xingling Wang
    Qing Yu
    Guanyu Liu
    Enuo Cui
    Tao Yu
    Xiran Jiang
    Abdominal Radiology, 2021, 46 : 5072 - 5085
  • [10] Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease
    Xiao, Longyang
    Zhao, Haichen
    Liu, Shunli
    Dong, Wenlu
    Gao, Yuanxiang
    Wang, Lili
    Huang, Baoxiang
    Li, Zhiming
    ABDOMINAL RADIOLOGY, 2024, 49 (04) : 1165 - 1174