Skip to main content

Advertisement

SpringerLink
Log in

A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn’s disease

  • Hollow Organ GI
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objectives

Differentiating intestinal tuberculosis (ITB) from Crohn’s disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach for distinguishing ITB from CD.

Methods

Eighty-two patients including 25 patients with ITB and 57 patients with CD were retrospectively recruited (54 in training cohort and 28 in testing cohort). The region of interest (ROI) for the lesion was delineated on magnetic resonance enterography (MRE) and colonoscopy images. Radiomic features were extracted by least absolute shrinkage and selection operator regression. Pathological feature was extracted automatically by deep-learning method. Clinical features were filtered by logistic regression analysis. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Delong’s test was applied to compare the efficiency between the multidisciplinary-based model and the other four single-disciplinary-based models.

Results

The radiomics model based on MRE features yielded an AUC of 0.87 (95% confidence interval [CI] 0.68–0.96) on the test data set, which was similar to the clinical model (AUC, 0.90 [95% CI 0.71–0.98]) and higher than the colonoscopy radiomics model (AUC, 0.68 [95% CI 0.48–0.84]) and pathology deep-learning model (AUC, 0.70 [95% CI 0.49–0.85]). Multidisciplinary model, integrating 3 clinical, 21 MRE radiomic, 5 colonoscopy radiomic, and 4 pathology deep-learning features, could significantly improve the diagnostic performance (AUC of 0.94, 95% CI 0.78–1.00) on the bases of single-disciplinary-based models. DCA confirmed the clinical utility.

Conclusions

Multidisciplinary-based model integrating clinical, MRE, colonoscopy, and pathology features was useful in distinguishing ITB from CD.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

CD:

Crohn’s disease

ITB:

Intestinal tuberculosis

ATT:

Anti-tubercular therapy

MRE:

Magnetic resonance enterography

CTE:

Computed tomography enterography

T1WIT1:

Weighted image

T2WI:

T2 Weighted image

DWI:

Diffusion weighted imaging

ADC:

Apparent diffusion coefficient

VOI:

Volume of interest

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

RF:

Random forest

ROI:

Region of interest

CNN:

Convolutional neural network

PCA:

Principal component analysis

AUC:

Receiver operating characteristic curve

CI:

Confidence interval

DCA:

Decision curve analysis

NRI:

Net reclassification improvement

IDI:

Integrated discrimination improvement

References

  1. Ng, S.C., H.Y. Shi, N. Hamidi, et al., Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet, 2017. 390(10114): p. 2769-2778.

    Article  PubMed  Google Scholar 

  2. Limsrivilai, J. and N. Pausawasdi, Intestinal tuberculosis or Crohn's disease: a review of the diagnostic models designed to differentiate between these two gastrointestinal diseases. Intest Res, 2021. 19(1): p. 21-32.

    Article  PubMed  Google Scholar 

  3. Kirsch, R., M. Pentecost, M. Hall Pde, et al., Role of colonoscopic biopsy in distinguishing between Crohn's disease and intestinal tuberculosis. J Clin Pathol, 2006. 59(8): p. 840–4.

  4. Acharya, B., A. Acharya, S. Gautam, et al., Advances in diagnosis of Tuberculosis: an update into molecular diagnosis of Mycobacterium tuberculosis. Mol Biol Rep, 2020. 47(5): p. 4065-4075.

    Article  CAS  PubMed  Google Scholar 

  5. Maaser, C., A. Sturm, S.R. Vavricka, et al., ECCO-ESGAR Guideline for Diagnostic Assessment in IBD Part 1: Initial diagnosis, monitoring of known IBD, detection of complications. J Crohns Colitis, 2019. 13(2): p. 144-164.

    Article  PubMed  Google Scholar 

  6. Kedia, S., R. Sharma, S. Bopanna, G. Makharia, and V. Ahuja, Predictive Model for Differentiating Crohn's Disease and Intestinal Tuberculosis: The Story Is Incomplete Without Imaging. Am J Gastroenterol, 2017. 112(1): p. 188-189.

    Article  PubMed  Google Scholar 

  7. Yu, H., Y. Liu, Y. Wang, et al., Clinical, endoscopic and histological differentiations between Crohn's disease and intestinal tuberculosis. Digestion, 2012. 85(3): p. 202-9.

    Article  PubMed  Google Scholar 

  8. Zhu, C., Y. Yu, S. Wang, et al., A Novel Clinical Radiomics Nomogram to Identify Crohn's Disease from Intestinal Tuberculosis. J Inflamm Res, 2021. 14: p. 6511-6521.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Messaris, E., N. Chandolias, D. Grand, and V. Pricolo, Role of magnetic resonance enterography in the management of Crohn disease. Arch Surg, 2010. 145(5): p. 471-5.

    Article  PubMed  Google Scholar 

  10. He, Y., Z. Zhu, Y. Chen, et al., Development and Validation of a Novel Diagnostic Nomogram to Differentiate Between Intestinal Tuberculosis and Crohn's Disease: A 6-year Prospective Multicenter Study. Am J Gastroenterol, 2019. 114(3): p. 490-499.

    Article  PubMed  Google Scholar 

  11. Li, X.H., R. Mao, S.Y. Huang, et al., Characterization of Degree of Intestinal Fibrosis in Patients with Crohn Disease by Using Magnetization Transfer MR Imaging. Radiology, 2018. 287(2): p. 494-503.

    Article  PubMed  Google Scholar 

  12. Mao, R., W.D. Liao, Y. He, et al., Computed tomographic enterography adds value to colonoscopy in differentiating Crohn's disease from intestinal tuberculosis: a potential diagnostic algorithm. Endoscopy, 2015. 47(4): p. 322-9.

    Article  PubMed  Google Scholar 

  13. Zhao, X.S., Z.T. Wang, Z.Y. Wu, et al., Differentiation of Crohn's disease from intestinal tuberculosis by clinical and CT enterographic models. Inflamm Bowel Dis, 2014. 20(5): p. 916-25.

    Article  PubMed  Google Scholar 

  14. Koo, T.K. and M.Y. Li, A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 2016. 15(2): p. 155-163.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Tibshirani, R., Regression shrinkage and selection via the lasso: a retrospective. 2011. 73(3): p. 273-282.

    Google Scholar 

  16. Breiman, L., Random Forests. Machine Learning, 2001. 45(1): p. 5-32.

    Article  Google Scholar 

  17. Lu, M.Y., D.F.K. Williamson, T.Y. Chen, et al., Data-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomedical Engineering, 2021. 5(6): p. 555-570.

    Article  PubMed  PubMed Central  Google Scholar 

  18. He, K., X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016.

  19. Abdi, H. and L.J. Williams, Principal component analysis. 2010. 2(4): p. 433-459.

    Google Scholar 

  20. DeLong, E.R., D.M. DeLong, and D.L. Clarke-Pearson, Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics, 1988. 44(3): p. 837-845.

    Article  CAS  PubMed  Google Scholar 

  21. Pencina, M.J., R.B. D' Agostino Sr, R.B. D' Agostino Jr, and R.S. Vasan, Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. 2008. 27(2): p. 157–172.

  22. Tabari, A., A. Kilcoyne, W.R. Jeck, M. Mino-Kenudson, and M.S. Gee, Texture Analysis of Magnetic Resonance Enterography Contrast Enhancement Can Detect Fibrosis in Crohn Disease Strictures. J Pediatr Gastroenterol Nutr, 2019. 69(5): p. 533-538.

    Article  PubMed  Google Scholar 

  23. Jung, Y., Y. Hwangbo, S.M. Yoon, et al., Predictive Factors for Differentiating Between Crohn's Disease and Intestinal Tuberculosis in Koreans. Am J Gastroenterol, 2016. 111(8): p. 1156-64.

    Article  PubMed  Google Scholar 

  24. Meng, Y., Y. Li, R. Hao, X. Li, and F. Lu, Analysis of Phenotypic Variables and Differentiation Between Untypical Crohn's Disease and Untypical Intestinal Tuberculosis. Dig Dis Sci, 2019. 64(7): p. 1967-1975.

    Article  PubMed  Google Scholar 

  25. Limsrivilai, J., A.B. Shreiner, A. Pongpaibul, et al., Meta-Analytic Bayesian Model For Differentiating Intestinal Tuberculosis from Crohn's Disease. Am J Gastroenterol, 2017. 112(3): p. 415-427.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kim, J.M., J.G. Kang, S. Kim, and J.H. Cheon, Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis. J Gastroenterol Hepatol, 2021. 36(8): p. 2141-2148.

    Article  PubMed  Google Scholar 

  27. Epstein, D., G. Watermeyer, and R. Kirsch, Review article: the diagnosis and management of Crohn's disease in populations with high-risk rates for tuberculosis. Aliment Pharmacol Ther, 2007. 25(12): p. 1373-88.

    Article  CAS  PubMed  Google Scholar 

  28. Kedia S, Das P, Madhusudhan KS, et al. Differentiating Crohn's disease from intestinal tuberculosis. World J Gastroenterol. 2019. 28;25(4):418–432.

Download references

Funding

This study was supported by the National Natural Science Foundation of China [Grant Nos. 82070680, 82072002, 82270693, 82271958, 62371303].

Author information

Author notes

  1. Baolan Lu, Zengan Huang, and Jinjiang Lin have 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, Guangdong, People’s Republic of China

    Baolan Lu, Jinjiang Lin, Ruonan Zhang, Xiaodi Shen, Lili Huang, Xinyue Wang, Weitao He, Jiayu Fang, Zhoulei Li, Shi-Ting Feng & Yangdi Wang

  2. Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, People’s Republic of China

    Zengan Huang & Bingsheng Huang

  3. Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, People’s Republic of China

    Qiapeng Huang

  4. Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, People’s Republic of China

    Ren Mao

  5. Department of Pathology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, People’s Republic of China

    Ziying Ye

  6. Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, People’s Republic of China

    Jian Zhang

  7. School of Biomedical Engineering, Health Science Center, Shenzhen University, Guangdong, People’s Republic of China

    Jian Zhang

Authors
  1. Baolan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zengan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinjiang Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ruonan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaodi Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lili Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xinyue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Weitao He
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Qiapeng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jiayu Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ren Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Zhoulei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Bingsheng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Shi-Ting Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Ziying Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yangdi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ziying Ye, Jian Zhang or Yangdi Wang.

Ethics declarations

Conflict of interest

All authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 96 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, B., Huang, Z., Lin, J. et al. A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn’s disease. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04307-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00261-024-04307-7

Keywords

Search

Navigation