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Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease

  • Hepatobiliary
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop and compare radiomics model and fusion model based on multiple MR parameters for staging liver fibrosis in patients with chronic liver disease.

Materials and methods

Patients with chronic liver disease who underwent multiparametric abdominal MRI were included in this retrospective study. Multiparametric MR images were imported into 3D-Slicer software for drawing bounding boxes on MR images. By using a 3D-Slicer extension of SlicerRadiomics, radiomics features were extracted from these MR images. The z-score normalization method was used for post-processing radiomics features. The least absolute shrinkage and selection operator method (LASSO) was performed for selecting significant radiomics features. The logistic regression analysis was used for building the radiomics model. A fusion model was built by integrating serum fibrosis biomarkers of aspartate transaminase-to-platelet ratio index (APRI) and the fibrosis-4 index (FIB-4) with radiomics signatures.

Results

In the training cohort, AUCs of radiomics and fusion model were 0.707–0.842 and 0.718–0.854 for differentiating different groups. In the testing cohort, AUCs were 0.514–0.724 and 0.609–0.728. For the training cohort, there was no significant difference of AUCs between radiomics and fusion model (p > 0.05). For the testing cohort, AUCs of fusion model were higher than those of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4 (p = 0.011 & 0.042).

Conclusions

Radiomics model and fusion model based on multiparametric MRI exhibited the feasibility for staging liver fibrosis in patients with CLD, and APRI and FIB-4 could improve the diagnostic performance of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4.

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Abbreviations

ECM:

Extracellular matrix

MRE:

Magnetic resonance elastography

RFI:

Radiomics fibrosis index

AUC:

Area under the curves

PACS:

Picture archiving and communication system

TACE:

Transcatheter arterial chemoebolization

APRI:

Aspartate transaminase-to-platelet ratio index

FIB-4:

Fibrosis-4 index

ROI:

Region of interest

GLCM:

Gray level cooccurrence matrix

GLDM:

Gray level dependence matrix

GLRLM:

Gray level run length matrix

GLSZM:

Gray level size zone matrix

NGTDM:

Neighbouring gray tone difference matrix

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator method

CI:

Confidence interval

CLD:

Chronic liver disease

References

  1. Roehlen N, Crouchet E, Baumert TF (2020). Liver Fibrosis: Mechanistic Concepts and Therapeutic Perspectives. Cells 9:875

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Caligiuri A, Gentilini A, Pastore M, et al (2021). Cellular and Molecular Mechanisms Underlying Liver Fibrosis Regression. Cells 10:2759

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rockey DC, Caldwell SH, Goodman ZD, et al (2009). Liver biopsy. Hepatology 49:1017–1044

    Article  PubMed  Google Scholar 

  4. Standish RA, Cholongitas E, Dhillon A, et al (2006). An appraisal of the histopathological assessment of liver fibrosis. Gut 55:569–578

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Fu J, Wu B, Wu H, et al (2020). Accuracy of real-time shear wave elastography in staging hepatic fibrosis: a meta-analysis. BMC Med Imaging 20:16

    Article  PubMed  PubMed Central  Google Scholar 

  6. Jiang W, Huang S, Teng H, et al (2018). Diagnostic accuracy of point shear wave elastography and transient elastography for staging hepatic fibrosis in patients with non-alcoholic fatty liver disease: a meta-analysis. BMJ Open 8:e021787

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ozturk A, Olson MC, Samir AE, Venkatesh SK (2022). Liver fibrosis assessment: MR and US elastography. Abdom Radiol (NY) 47:3037–3050

    Article  PubMed  Google Scholar 

  8. Kim YS, Jang YN, Song JS (2018). Comparison of gradient-recalled echo and spin-echo echo-planar imaging MR elastography in staging liver fibrosis: a meta-analysis. Eur Radiol 28:1709–1718

    Article  PubMed  Google Scholar 

  9. Manduca A, Bayly PJ, Ehman RL, et al (2021). MR elastography: Principles, guidelines, and terminology. Magn Reson Med 85:2377–2390

    Article  PubMed  Google Scholar 

  10. Lafata KJ, Wang Y, Konkel B, et al (2022). Radiomics: a primer on high-throughput image phenoty**. Abdom Radiol (NY) 47:2986–3002

    Article  PubMed  Google Scholar 

  11. Park HJ, Lee SS, Park B, et al (2019). Radiomics Analysis of Gadoxetic Acid-enhanced MRI for Staging Liver Fibrosis. Radiology 290:380–387

    Article  PubMed  Google Scholar 

  12. Zheng W, Guo W, **ong M, et al (2023). Clinic-radiological features and radiomics signatures based on Gd-BOPTA-enhanced MRI for predicting advanced liver fibrosis. Eur Radiol 33:633–644

    Article  CAS  PubMed  Google Scholar 

  13. Zhang D, Cao Y, Sun Y, et al (2023). Radiomics nomograms based on R2* map** and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study. Eur Radiol 33:1653–1667

    Article  PubMed  Google Scholar 

  14. Wei H, Shao Z, Fu F, et al (2023). Value of multimodal MRI radiomics and machine learning in predicting staging liver fibrosis and grading inflammatory activity. Br J Radiol 96:20220512

    Article  PubMed  Google Scholar 

  15. Bedossa P, Bioulacsage P, Callard P, et al (1994). Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C. Hepatology 20:15–20

    Article  Google Scholar 

  16. Goodman ZD (2007). Grading and staging systems for inflammation and fibrosis in chronic liver diseases. J Hepatol 47:598–607

    Article  PubMed  Google Scholar 

  17. Wai CT, Greenson Jk, Fontana RJ, et al (2003). A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 38:518–526

    Article  PubMed  Google Scholar 

  18. Sterling RK, Lissen E, Clumeck N, et al (2006). Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 43:1317–1325

    Article  CAS  PubMed  Google Scholar 

  19. Koo TK, Li MY (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 15:155–163

    Article  PubMed  PubMed Central  Google Scholar 

  20. **ao G, Yang J, Yan L (2015). Comparison of diagnostic accuracy of aspartate aminotransferase to platelet ratio index and fibrosis-4 index for detecting liver fibrosis in adult patients with chronic hepatitis B virus infection: a systemic review and meta-analysis. Hepatology 61:292–302

    Article  PubMed  Google Scholar 

  21. Kim WR, Berg T, Asselah T, et al (2016). Evaluation of APRI and FIB-4 scoring systems for non-invasive assessment of hepatic fibrosis in chronic hepatitis B patients. J Hepatol 64:773–780

    Article  CAS  PubMed  Google Scholar 

  22. Orlhac F, Lecler A, Savatovski J, et al (2021). How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol 31:2272–2280

    Article  PubMed  Google Scholar 

  23. Carre A, Battistella E, Niyoteka S, et al (2022). AutoComBat: a generic method for harmonizing MRI-based radiomic features. Sci Rep 12:12762

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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Author notes

  1. Longyang **ao and Haichen Zhao contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China

    Longyang **ao, Haichen Zhao, Shunli Liu, Wenlu Dong, Yuanxiang Gao & Zhiming Li

  2. Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China

    Lili Wang

  3. College of Computer Science and Technology of Qingdao University, No.308 Ningxia Road, Qingdao, 266071, China

    Baoxiang Huang

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  1. Longyang **ao
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  2. Haichen Zhao
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  3. Shunli Liu
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Correspondence to Baoxiang Huang or Zhiming Li.

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**ao, L., Zhao, H., Liu, S. et al. Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease. Abdom Radiol 49, 1165–1174 (2024). https://doi.org/10.1007/s00261-023-04142-2

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