Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Objective:

To investigate the feasibility of image characteristics and radiomics combined with machine learning based on Gd-EOB-DTPA-enhanced MRI for functional liver reserve assessment in cirrhotic patients.

Materials and Methods

123 patients with cirrhosis were retrospectively analyzed; all our patients underwent pre-contrast MRI, triphasic (arterial phase, venous phase, equilibrium phase) Gd-EOB-DTPA dynamic enhancement and hepatobiliary phase (20 minutes delayed). The relative enhancement (RE) of the patient's liver, the liver-spleen signal ratio in the hepatobiliary phase (SI liver/ spleen), the liver-vertical muscle signal ratio in the hepatobiliary phase (SI liver/ muscle), the bile duct signal intensity contrast ratio (SIR), and the radiomics features were evaluated. The support vector machine (SVM) was used as the core of machine learning to construct the liver function classification model using image and radiomics characteristics, respectively.

Results:

The area under the curve was the largest in SIR to identify Child-Pugh group A Child-Pugh group B+C in the image characteristics, AUC = 0.740, and Perc. 10% to identify Child-Pugh group A Child-Pugh group B+C in the radiomics characteristics, AUC = 0.9337. The efficacy of the SVM model constructed using radiomics characteristics was better, with an area under the curve of 0.918, a sensitivity of 95.45%, a specificity of 80.00%, and an accuracy of 89.19%.

Conclusion:

The image and radiomics characteristics based on Gd-EOB-DTPA-enhanced MRI can reflect liver function, and the model constructed based on radiomics characteristics combined with machine learning methods can better assess functional liver reserve.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056281405240104155500
2024-01-01
2025-01-06

  • From This Site

    /content/journals/cmir/10.2174/0115734056281405240104155500
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cmir/20/1/e15734056281405.html?itemId=/content/journals/cmir/10.2174/0115734056281405240104155500&mimeType=html&fmt=ahah

References

  1. WangF.S. FanJ.G. ZhangZ. GaoB. WangH.Y. The global burden of liver disease: The major impact of China.Hepatology20146062099210810.1002/hep.2740625164003
     [Google Scholar]
  2. HaimerlM. VerlohN. ZemanF. FellnerC. NickelD. LangS.A. TeufelA. StroszczynskiC. WiggermannP. Gd-EOB-DTPA-enhanced MRI for evaluation of liver function: Comparison between signal-intensity-based indices and T1 relaxometry.Sci. Rep.2017714334710.1038/srep4334728266528
     [Google Scholar]
  3. ChenGY LiZL Application of diffusion-weighted imaging and hepatobiliary-specific contrast agent Gd-EOB-DTPA in the diagnosis and differential diagnosis of focal liver lesionsSI Department and ax u ex UE BAOy IX UE ban202253573774310.12182/20220960205
     [Google Scholar]
  4. FengZ. LiH. LiuQ. DuanJ. ZhouW. YuX. ChenQ. LiuZ. WangW. RongP. CT radiomics to predict macrotrabecular-massive subtype and immune status in hepatocellular carcinoma.Radiology20233071e22129110.1148/radiol.22129136511807
     [Google Scholar]
  5. MeyerH.J. LeonhardiJ. HöhnA.K. PappischJ. WirtzH. DeneckeT. FrilleA. CT texture analysis of pulmonary neuroendocrine tumors—associations with tumor grading and proliferation.J. Clin. Med.20211023557110.3390/jcm1023557134884272
     [Google Scholar]
  6. TanakaT. NishidaH. MieK. YamazakiH. LinL.S. AkiyoshiH. Assessment of hepatitis and fibrosis using Gd-EOB-DTPA MRI in dogs.Vet. Rec. Open202071e00037110.1136/vetreco‑2019‑00037132818058
     [Google Scholar]
  7. VerlohN. UtpatelK. HaimerlM. ZemanF. FellnerC. Fichtner-FeiglS. TeufelA. StroszczynskiC. EvertM. WiggermannP. Liver fibrosis and Gd-EOB-DTPA-enhanced MRI: A histopathologic correlation.Sci. Rep.2015511540810.1038/srep1540826478097
     [Google Scholar]
  8. KankiA. TamadaT. HigakiA. NodaY. TanimotoD. SatoT. HigashiH. ItoK. Hepatic parenchymal enhancement at Gd-EOB-DTPA-enhanced MR imaging: Correlation with morphological grading of severity in cirrhosis and chronic hepatitis.Magn. Reson. Imaging201230335636010.1016/j.mri.2011.11.00222227353
     [Google Scholar]
  9. WangX.X. HuJ.H. LiuJ.Y. ChenJ.F. HuangJ.C. LuX.F. Value of gadoxetic acid disodium-enhanced MRI hepatobiliary phase parameters in evaluating liver function in cirrhotic patients.Imaging Res. Med. Appl.2022606737510.3969/j.issn.2096‑3807.2022.06.025
     [Google Scholar]
  10. YangM. ZhangY. ZhaoW. ChengW. WangH. GuoS. Evaluation of liver function using liver parenchyma, spleen and portal vein signal intensities during the hepatobiliary phase in Gd-EOB-D TPA-enhanced MRI.BMC Med. Imaging202020111910.1186/s12880‑020‑00519‑733081713
     [Google Scholar]
  11. ShaoJ. Application of gadoxetic acid disodium-enhanced magnetic resonance imaging to assess liver function.Shanxi Medical University2021
     [Google Scholar]
  12. LeeH.J. HongS.B. LeeN.K. KimS. SeoH.I. KimD.U. HanS.Y. ChooK.S. Validation of functional liver imaging scores (FLIS) derived from gadoxetic acid–enhanced MRI in patients with chronic liver disease and liver cirrhosis: The relationship between child-pugh score and FLIS.Eur. Radiol.202131118606861410.1007/s00330‑021‑07955‑133881570
     [Google Scholar]
  13. ZhouW. HuH.J. ShenB. ShenW.Q. ChenH. WuX. Application value of quantitative assessment of liver reserve function in cirrhotic patients based on gadoxetic acid disodium enhanced magnetic resonance imaging imaging histology.Chin. Med. Sci.2020420445946710.3881/j.issn.1000‑503X.1262032895097
     [Google Scholar]
  14. ShiZ. CaiW. FengX. CaiJ. LiangY. XuJ. ZhenJ. LiangX. Radiomics analysis of Gd-EOB-DTPA enhanced hepatic MRI for assessment of functional liver reserve.Acad. Radiol.202229221321810.1016/j.acra.2021.04.01934183230
     [Google Scholar]
  15. LubnerM.G. MaleckiK. KlokeJ. GaneshanB. PickhardtP.J. Texture analysis of the liver at MDCT for assessing hepatic fibrosis.Abdom. Radiol.20174282069207810.1007/s00261‑017‑1096‑528314916
     [Google Scholar]
  16. ZhangZ.X. HuangZ.J. HeS. WangJ. LiangM.X. YangX.F. Application of enhanced CT imaging histology-based assessment of hepatic reserve function in cirrhotic patients.Radiol. Prat.20223706676682[J].10.13609/j.cnki.1000‑0313.2022.06.002
     [Google Scholar]
  17. ParkE.J. KimS.H. ParkS.J. BaekT.W. Texture analysis of gray-scale ultrasound images for staging of hepatic fibrosis.J. Korean Soc. Radiol.202182111612710.3348/jksr.2019.018536237456
     [Google Scholar]
  18. ZhaoR. GongX.J. GeY.Q. ZhaoH. WangL.S. YuH.Z. LiuB. Use of texture analysis on noncontrast MRI in classification of early stage of liver fibrosis.Can. J. Gastroenterol. Hepatol.202120211910.1155/2021/667782133791254
     [Google Scholar]
  19. InchingoloR. FalettiR. GrazioliL. TricaricoE. GattiM. PecorelliA. IppolitoD. MR with Gd-EOB-DTPA in assessment of liver nodules in cirrhotic patients.World J. Hepatol.201810746247310.4254/wjh.v10.i7.46230079132
     [Google Scholar]
  20. XuL. WenX. FengX. KangY. Application progress of Gd-EOB-DTPA-enhanced MRI T1 mapping in hepatic diffuse diseases.Curr. Med. Imaging Rev.202218121276128110.2174/157340561766621113015345034847846
     [Google Scholar]
  21. DingC. JiaJ. BaiG. ZhouW. ShanW. Predictive value of Gd-EOB-DTPA -enhanced magnetic resonance imaging for post-hepatectomy liver failure: A systematic review and meta-analysis.Acta Radiol.20236441347135610.1177/0284185122113448536303435
     [Google Scholar]
  22. LeeS. ChoiD. JeongW.K. Hepatic enhancement of Gd‐EOB‐DTPA‐enhanced 3 Tesla MR imaging: Assessing severity of liver cirrhosis.J. Magn. Reson. Imaging20164451339134510.1002/jmri.2528827197633
     [Google Scholar]
  23. TamadaT. ItoK. SoneT. KankiA. SatoT. HigashiH. Gd-EOB-DTPA enhanced MR imaging: Evaluation of biliary and renal excretion in normal and cirrhotic livers.Eur. J. Radiol.2011803e207e21110.1016/j.ejrad.2010.08.03320869827
     [Google Scholar]
  24. NodaY. GoshimaS. KajitaK. KawadaH. KawaiN. KoyasuH. MatsuoM. BaeK.T. Biliary tract enhancement in gadoxetic acid-enhanced MRI correlates with liver function biomarkers.Eur. J. Radiol.201685112001200710.1016/j.ejrad.2016.09.00327776652
     [Google Scholar]
  25. GilliesR.J. KinahanP.E. HricakH. Radiomics: Images are more than pictures, they are data.Radiology2016278256357710.1148/radiol.201515116926579733
     [Google Scholar]
  26. MayerhoeferM.E. MaterkaA. LangsG. HäggströmI. SzczypińskiP. GibbsP. CookG. Introduction to radiomics.J. Nucl. Med.202061448849510.2967/jnumed.118.22289332060219
     [Google Scholar]
  27. DengX.Y. ChenH.Y. YuJ.N. ZhuX.L. ChenJ.Y. ShaoG.L. YuR.S. Diagnostic value of CT- and MRI-based texture analysis and imaging findings for grading cartilaginous tumors in long bones.Front. Oncol.20211170020410.3389/fonc.2021.70020434722248
     [Google Scholar]
  28. MengX. LiS. HeK. HuH. FengC. LiZ. WangY. Evaluation of whole-tumor texture analysis based on MRI diffusion kurtosis and biparametric VI-RADS model for staging and grading bladder cancer.Bioengineering202310774510.3390/bioengineering1007074537508772
     [Google Scholar]
  29. LiuY. FangQ. JiangA. MengQ. PangG. DengX. Texture analysis based on U-Net neural network for intracranial hemorrhage identification predicts early enlargement.Comput. Methods Programs Biomed.202120610614010.1016/j.cmpb.2021.10614033979753
     [Google Scholar]
  30. MuraokaH. KanedaT. HiraharaN. ItoK. OkadaS. KondoT. Efficacy of magnetic resonance imaging texture features of the lateral pterygoid muscle in distinguishing rheumatoid arthritis and osteoarthritis of the temporomandibular joint.Dentomaxillofac. Radiol.20235232022032110.1259/dmfr.2022032136594821
     [Google Scholar]
  31. CamachoD.M. CollinsK.M. PowersR.K. CostelloJ.C. CollinsJ.J. Next-generation machine learning for biological networks.Cell201817371581159210.1016/j.cell.2018.05.01529887378
     [Google Scholar]
  32. MikesS. HaindlM. Texture segmentation benchmark.IEEE Trans. Pattern Anal. Mach. Intell.2021449110.1109/TPAMI.2021.307591633905324
     [Google Scholar]
  33. HeL. LiH. DudleyJ.A. MaloneyT.C. BradyS.L. SomasundaramE. TroutA.T. DillmanJ.R. Machine learning prediction of liver stiffness using clinical and T2-weighted MRI radiomic data.AJR Am. J. Roentgenol.2019213359260110.2214/AJR.19.2108231120779
     [Google Scholar]
  34. SchawkatK. CiritsisA. von UlmensteinS. Honcharova-BiletskaH. JüngstC. WeberA. GublerC. MertensJ. ReinerC.S. Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology.Eur. Radiol.20203084675468510.1007/s00330‑020‑06831‑832270315
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056281405240104155500
Loading
Gd-EOB-DTPA-enhanced MRI Image Characteristics and Radiomics Characteristics Combined with Machine Learning for Assessment of Functional Liver Reserve
Curr. Med. Imaging 20, e15734056281405 (2024); https://doi.org/10.2174/0115734056281405240104155500
/content/journals/cmir/10.2174/0115734056281405240104155500
/content/journals/cmir/10.2174/0115734056281405240104155500
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Cirrhosis; Functional liver reserve; Gd-EOB-DTPA; Machine learning methods; MRI; Radiomics

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test