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Intestinal fibrosis classification in patients with Crohn’s disease using CT enterography–based deep learning: comparisons with radiomics and radiologists

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Accurate evaluation of bowel fibrosis in patients with Crohn’s disease (CD) remains challenging. Computed tomography enterography (CTE)–based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently.

Methods

We enrolled 312 bowel segments of 235 CD patients (median age, 33 years old) from three hospitals in this retrospective study. A training cohort and test cohort 1 were recruited from center 1, while test cohort 2 from centers 2 and 3. All patients performed CTE within 3 months before surgery. The histological fibrosis was semi-quantitatively assessed. A DLM was constructed in the training cohort based on a 3D deep convolutional neural network with 10-fold cross-validation, and external independent validation was conducted on the test cohorts. The radiomics model (RM) was developed with 4 selected radiomics features extracted from CTE images by using logistic regression. The evaluation of CTE images was performed by two radiologists. DeLong’s test and a non-inferiority test were used to compare the models’ performance.

Results

DLM distinguished none-mild from moderate-severe bowel fibrosis with an area under the receiver operator characteristic curve (AUC) of 0.828 in the training cohort and 0.811, 0.808, and 0.839 in the total test cohort, test cohorts 1 and 2, respectively. In the total test cohort, DLM achieved better performance than two radiologists (*1 AUC = 0.579, *2 AUC = 0.646; both p < 0.05) and was not inferior to RM (AUC = 0.813, p < 0.05). The total processing time for DLM was much shorter than that of RM (p < 0.001).

Conclusion

DLM is better than radiologists in diagnosing intestinal fibrosis on CTE in patients with CD and not inferior to RM; furthermore, it is more time-saving compared to RM.

Key Points

• Question Could computed tomography enterography (CTE)–based deep learning model (DLM) accurately distinguish intestinal fibrosis severity in patients with Crohn’s disease (CD)?

• Findings In this cross-sectional study that included 235 patients with CD, DLM achieved better performance than that of two radiologists’ interpretation and was not inferior to RM with significant differences and much shorter processing time.

• Meaning This DLM may accurately distinguish the degree of intestinal fibrosis in patients with CD and guide gastroenterologists to formulate individualized treatment strategies for those with bowel strictures.

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Abbreviations

AI:

Artificial intelligence

CD:

Crohn’s disease

CTE:

CT enterography

DLM:

Deep learning model

RM:

Radiomics model

VOI:

Volume of interest

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Acknowledgements

The authors thank Xinning Liu (a pathologist from The First Affiliated Hospital of Sun Yat-sen University) for her contribution to the histopathologic score for the test of the inter-pathologist consistency.

Funding

This study was supported by the National Natural Science Foundation of China (82070680, 82072002, 81600508, 81770654, 81771908, 81870451, 81802431, and 81972516), Natural Science Foundation of Guangdong Province (2018A030313050 and 2021A1515012448), Guangdong Basic and Applied Basic Research Foundation (2020A1515010571), and Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions (2019SHIBS0003). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Author information

Author notes

  1. Jixin Meng and Zixin Luo contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People’s Republic of China

    Jixin Meng, Baolan Lu, Mengchen Zhang, Yangdi Wang, Xiaodi Shen, Canhui Sun, Ziping Li, Shi-Ting Feng & Xuehua Li

  2. Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Block A2, Lihu Campus of Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518000, People’s Republic of China

    Zixin Luo, Chenglang Yuan, Qinqin Huang, Zhuya Zhang & Bingsheng Huang

  3. Department of Gastrointestinal and Pancreatic Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China

    Zhihui Chen

  4. Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Yuancun Er Heng Road, NO.26, Guangzhou, 510655, People’s Republic of China

    Jie Zhou, Zhiyang Zhou & Xiaochun Meng

  5. Department of Medical Imaging Center, Nan Fang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, People’s Republic of China

    Zhao Chen & Yikai Xu

  6. Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People’s Republic of China

    Ziyin Ye & Qinghua Cao

  7. Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan II Road, Guangzhou, 510080, People’s Republic of China

    Ren Mao & Minhu Chen

  8. Department of Gastroenterology, Hepatology and Nutrition, Digestive Diseases and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA

    Ren Mao

Authors
  1. Jixin Meng
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Corresponding authors

Correspondence to Bingsheng Huang or Xuehua Li.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr. Xuehua Li.

Conflict of interest

The authors declare no competing interests.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Approved by the institutional ethics review board of The First Affiliated Hospital of Sun Yat-sen University.

Study subjects or cohorts overlap

One hundred fourteen patients with 159 samples in the training cohort and 53 patients with 53 samples in test cohort 2 in this study came from our previous radiomics study Development and Validation of a Novel Computed-Tomography Enterography Radiomic Approach for Characterization of Intestinal Fibrosis in Crohn’s Disease.

Methodology

• retrospective

• cross sectional study

• multi-center study

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Meng, J., Luo, Z., Chen, Z. et al. Intestinal fibrosis classification in patients with Crohn’s disease using CT enterography–based deep learning: comparisons with radiomics and radiologists. Eur Radiol 32, 8692–8705 (2022). https://doi.org/10.1007/s00330-022-08842-z

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