Skip to main content
SpringerLink
Log in

Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease

European Radiology Aims and scope Submit manuscript

Cite this article

Abstract

Objectives

To investigate whether volumetric visceral adipose tissue (VAT) features extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approach are effective in differentiating Crohn’s disease (CD) and ulcerative colitis (UC).

Methods

This retrospective study enrolled 316 patients (mean age, 36.25 ± 13.58 [standard deviation]; 219 men) with confirmed diagnosis of CD and UC who underwent CT enterography between 2012 and 2021. Volumetric VAT was semi-automatically segmented on the arterial phase images. Radiomics analysis was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. We developed a 3D-CNN model using VAT imaging data from the training cohort. Clinical covariates including age, sex, modified body mass index, and disease duration that impact VAT were added to the machine learning model for adjustment. The model’s performance was evaluated on the testing cohort separating from the model’s development process by its discrimination and clinical utility.

Results

Volumetric VAT radiomics analysis with LASSO had the highest AUC value of 0.717 (95% CI, 0.614–0.820), though difference of diagnostic performance among the 3D-CNN model (AUC = 0.693; 95% CI, 0.587–0.798) and radiomics analysis with PCA (AUC = 0.662; 95% CI, 0.548–0.776) and LASSO have not reached statistical significance (all p > 0.05). The radiomics score was higher in UC than in CD on the testing cohort (mean ± SD, UC 0.29 ± 1.05 versus CD −0.60 ± 1.25; p < 0.001). The LASSO model with adjustment of clinical covariates reached an AUC of 0.775 (95%CI, 0.683–0.868).

Conclusion

The developed volumetric VAT–based radiomics and 3D-CNN models provided comparable and effective performance for the characterization of CD from UC.

Key Points

• High-output feature data extracted from volumetric visceral adipose tissue on CT enterography had an effective diagnostic performance for differentiating Crohn’s disease from ulcerative colitis.

• With adjustment of clinical covariates that cause difference in volumetric visceral adipose tissue, adjusted clinical machine learning model reached stronger performance when distinguishing Crohn’s disease patients from ulcerative colitis patients.

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
Fig. 5

Abbreviations

3D-CNN:

Three-dimensional convolutional neural network

AUC:

The area under the receiver operating characteristic curves

CD:

Crohn’s disease

CTE:

CT enterography

IBD:

Inflammatory bowel disease

LASSO:

The least absolute shrinkage and selection operator

PCA:

Principal component analysis

ROC:

The receiver operating curve

UC:

Ulcerative colitis

VAT:

Visceral adipose tissue

References

  1. Uko V, Vortia E, Achkar JP et al (2014) Impact of abdominal visceral adipose tissue on disease outcome in pediatric Crohn’s disease. Inflamm Bowel Dis 20:2286–2291

    Article  PubMed  Google Scholar 

  2. Dobre M, Milanesi E, Mănuc TE et al (2018) Differential intestinal mucosa transcriptomic biomarkers for Crohn’s disease and ulcerative colitis. J Immunol Res. https://doi.org/10.1155/2018/9208274

  3. Maconi G, Bolzoni E, Giussani A, Friedman AB, Duca P (2014) Accuracy and cost of diagnostic strategies for patients with suspected Crohn’s disease. J Crohns Colitis 8:1684–1692

    Article  PubMed  Google Scholar 

  4. Zulian A, Cancello R, Ruocco C et al (2013) Differences in visceral fat and fat bacterial colonization between ulcerative colitis and Crohn’s disease. An in vivo and in vitro study. PLoS One. https://doi.org/10.1371/journal.pone.0078495

  5. Jahnsen J, Falch JA, Mowinckel P, Aadland E (2003) Body composition in patients with inflammatory bowel disease: a population-based study. Am J Gastroenterol 98:1556–1562

    Article  PubMed  Google Scholar 

  6. Zhang TH, Ding C, Xie TB et al (2017) Skeletal muscle depletion correlates with disease activity in ulcerative colitis and is reversed after colectomy. Clin Nutr 36:1586–1592

    Article  PubMed  Google Scholar 

  7. Cushing KC, Kordbacheh H, Gee MS, Kambadakone A, Ananthakrishnan AN (2018) Sarcopenia is a novel predictor of the need for rescue therapy in hospitalized ulcerative colitis patients. J Crohns Colitis 12:1036–1041

    Article  PubMed  PubMed Central  Google Scholar 

  8. Erhayiem B, Dhingsa R, Hawkey CJ, Subramanian V (2011) Ratio of visceral to subcutaneous fat area is a biomarker of complicated Crohn’s disease. Clin Gastroenterol Hepatol 9:684–687

    Article  PubMed  Google Scholar 

  9. Bamba S, Inatomi O, Takahashi K et al (2021) Assessment of body composition from CT images at the level of the third lumbar vertebra in inflammatory bowel disease. Inflamm Bowel Dis 27:1435–1442

    Article  PubMed  Google Scholar 

  10. Büning C, von Kraft C, Hermsdorf M et al (2015) Visceral adipose tissue in patients with Crohn’s disease correlates with disease activity, inflammatory markers, and outcome. Inflamm Bowel Dis 21:2590–2597

    Article  PubMed  Google Scholar 

  11. Thiberge C, Charpentier C, Gillibert A et al (2018) Lower subcutaneous or visceral adiposity assessed by abdominal computed tomography could predict adverse outcome in patients with Crohn’s disease. J Crohns Colitis 12:1429–1437

    Article  PubMed  Google Scholar 

  12. Zulian A, Cancello R, Micheletto G et al (2012) Visceral adipocytes: old actors in obesity and new protagonists in Crohn’s disease? Gut 61:86–94

    Article  CAS  PubMed  Google Scholar 

  13. Rigiroli F, Hoye J, Lerebours R et al (2021) CT radiomic features of superior mesenteric artery involvement in pancreatic ductal adenocarcinoma: a pilot study. Radiology 301:610–622

    Article  PubMed  Google Scholar 

  14. Attanasio S, Forte SM, Restante G, Gabelloni M, Guglielmi G, Neri E (2020) Artificial intelligence, radiomics and other horizons in body composition assessment. Quant Imaging Med Surg 10:1650–1660

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kurowski JA, Barbur I, Gupta R et al (2018) Radiomic texture analysis shows differential expression within visceral adipose tissue regions on MRI reflecting severity of pediatric Crohn’s disease. Gastroenterology 6:S448–S448

    Article  Google Scholar 

  16. Li X, Liang D, Meng J et al (2021) Development and validation of a novel computed-tomography enterography radiomic approach for characterization of intestinal fibrosis in Crohn’s disease. Gastroenterology 160:2303–2316

    Article  PubMed  Google Scholar 

  17. Shi J, Bao G, Hong J et al (2021) Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects. EBioMedicine. https://doi.org/10.1016/j.ebiom.2021.103471

  18. Truhn D, Schrading S, Haarburger C, Schneider H, Merhof D, Kuhl C (2019) Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI. Radiology 290:290–297

    Article  PubMed  Google Scholar 

  19. Li H, Mo Y, Huang C et al (2021) An MSCT-based radiomics nomogram combined with clinical factors can identify Crohn’s disease and ulcerative colitis. Ann Transl Med 9:572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bernstein CN, Eliakim A, Fedail S et al (2016) World Gastroenterology Organisation global guidelines inflammatory bowel disease: update August 2015. J Clin Gastroenterol 50:803–888

    Article  PubMed  Google Scholar 

  21. Satsangi J, Silverberg MS, Vermeire S, Colombel JF (2006) The Montreal classification of inflammatory bowel disease: controversies, consensus, and implications. Gut 55:749–753

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhou Z, Xiong Z, Xie Q et al (2021) Computed tomography-based multiple body composition parameters predict outcomes in Crohn’s disease. Insights Imaging. https://doi.org/10.1186/s13244-021-01083-6

  23. Zhou Z, Lu J, Morelli JN et al (2021) Utility of noncontrast MRI in the detection and risk grading of gastrointestinal stromal tumor: a comparison with contrast-enhanced CT. Quant Imaging Med Surg 11:2453–2464

    Article  PubMed  PubMed Central  Google Scholar 

  24. Taylor SA, Avni F, Cronin CG et al (2017) The first joint ESGAR/ ESPR consensus statement on the technical performance of cross-sectional small bowel and colonic imaging. Eur Radiol 27:2570–2582

    Article  CAS  PubMed  Google Scholar 

  25. Lin L, Dekkers IA, Huang L et al (2021) Renal sinus fat volume in type 2 diabetes mellitus is associated with glycated hemoglobin and metabolic risk factors. J Diabetes Complicat. https://doi.org/10.1016/j.jdiacomp.2021.107973

  26. Zwanenburg A, Vallieres M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338

    Article  PubMed  Google Scholar 

  27. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  PubMed  PubMed Central  Google Scholar 

  28. Bernatz S, Ackermann J, Mandel P et al (2020) Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features. Eur Radiol 30:6757–6769

    Article  PubMed  PubMed Central  Google Scholar 

  29. Wang W, Cao K, Jin S, Zhu X, Ding J, Peng W (2020) Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis. Eur Radiol 10:5738–5747

    Article  Google Scholar 

  30. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C et al (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-12-77

  31. Magudia K, Bridge CP, Bay CP et al (2021) Population-scale CT-based body composition analysis of a large outpatient population using deep learning to derive age-, sex-, and race-specific reference curves. Radiology 298:319–329

    Article  PubMed  Google Scholar 

  32. Engvall IL, Tengstrand B, Brismar K, Hafström I (2010) Infliximab therapy increases body fat mass in early rheumatoid arthritis independently of changes in disease activity and levels of leptin and adiponectin: a randomised study over 21 months. Arthritis Res Ther. https://doi.org/10.1186/ar3169

  33. Goeman JJ, Solari A (2014) Multiple hypothesis testing in genomics. Stat Med 33:1946–1978

    Article  PubMed  Google Scholar 

  34. Van Der Sloot KW, Joshi AD, Bellavance DR et al (2017) Visceral adiposity, genetic susceptibility, and risk of complications among individuals with Crohn’s disease. Inflamm Bowel Dis 23:82–88

    Article  PubMed  Google Scholar 

  35. Li XH, Feng ST, Cao QH et al (2021) 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 15:1161–1173

    Article  PubMed  PubMed Central  Google Scholar 

  36. Grillot J, D’Engremont C, Parmentier AL et al (2020) Sarcopenia and visceral obesity assessed by computed tomography are associated with adverse outcomes in patients with Crohn’s disease. Clin Nutr 39:3024–3030

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Xinyueyuan Hao and Fangqin Tan for the segmentation of the visceral adipose tissue. The authors thank Yuhan Tang from the School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, China, for advice on statistical analysis.

Funding

This work is supported by grants from the National Natural Science Foundation of China (NSFC), Nos. 82071890 and 82071889.

Author information

Authors and Affiliations

  1. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, Hubei, China

    Ziling Zhou, Ziman Xiong, Yuanqiu Li, Daoyu Hu, Xuemei Hu, Yaqi Shen & Zhen Li

  2. Biomedical Engineering Department, College of Life Sciences and Technology, Huazhong University of Science and Technology, Wuhan, China

    Ziling Zhou, Ran Cheng, Qingguo Xie & Peng Xiao

  3. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

    Qingyu Luo

Authors
  1. Ziling Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziman Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ran Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qingyu Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuanqiu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qingguo Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Peng Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Daoyu Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xuemei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yaqi Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Zhen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Peng Xiao or Yaqi Shen.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Yaqi Shen.

Conflict of interest

The authors of this manuscript declare that they have no conflict of interest.

Statistics and biometry

Two of the authors have significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• observational/diagnostic study

• performed at one institution

Additional information

Publisher’s note

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

Supplementary Information

ESM 1

(DOCX 4688 kb)

Rights and permissions

Springer Nature or its licensor 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

Zhou, Z., Xiong, Z., Cheng, R. et al. Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease. Eur Radiol 33, 1862–1872 (2023). https://doi.org/10.1007/s00330-022-09171-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-022-09171-x

Keywords

search

Navigation