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Advances in functional magnetic resonance and radiometrics in liver transplantation
CHEN Haoyuan  ZHANG Hui  WANG Yongfang 

Cite this article as: CHEN H Y, ZHANG H, WANG Y F. Advances in functional magnetic resonance and radiometrics in liver transplantation[J]. Chin J Magn Reson Imaging, 2023, 14(10): 171-176. DOI:10.12015/issn.1674-8034.2023.10.031.

[Abstract] Liver transplantation has become an effective treatment for end-stage liver disease. Accurate evaluation of liver function before and after liver transplantation and judgment of relapse after liver transplantation are the key points of clinical diagnosis and treatment. Functional magnetic resonance imaging (fMRI) techniques and radiomics such as magnetic resonance diffusion-weighted imaging, diffusion kurtosis imaging, blood oxygen level dependent imaging, magnetic resonance spectroscopy, proton fat density fraction and magnetic resonance elastography can noninvasionally evaluate the transplanted liver in terms of diffusion, oxygenation, metabolism, fat quantification, liver hardness, etc. To provide more information for the evaluation of liver function before and after liver transplantation and the judgment of relapse after liver transplantation. Its clinical value lies in early detection of liver function impairment, assessment of liver function injury degree and prediction of recurrence after liver transplantation, thus helping clinicians to diagnose disease early, formulate optimal diagnosis and treatment plan for patients and monitor drug efficacy, so as to improve the quality of life of patients. At the same time, it will gradually become a research hotspot in the future because it is new and non-invasive and can reveal pathological changes of liver transplantation. This article reviews the current status of fMRI and radiomics in evaluating liver transplantation, in order to provide reference for clinicians to predict prognosis and make treatment decisions, and to guide future research direction.
[Keywords] liver transplantation;recurrence;acute cellular rejection;magnetic resonance imaging;functional magnetic resonance imaging;radiomics;prognosis

CHEN Haoyuan1   ZHANG Hui2*   WANG Yongfang2  

1 College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China

2 Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China

Corresponding author: ZHANG H, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82001807).
Received  2023-06-05
Accepted  2023-09-28
DOI: 10.12015/issn.1674-8034.2023.10.031
Cite this article as: CHEN H Y, ZHANG H, WANG Y F. Advances in functional magnetic resonance and radiometrics in liver transplantation[J]. Chin J Magn Reson Imaging, 2023, 14(10): 171-176. DOI:10.12015/issn.1674-8034.2023.10.031.

HUGHES C B, HUMAR A. Liver transplantation: current and future[J]. Abdom Radiol (NY), 2021, 46(1): 2-8. DOI: 10.1007/s00261-019-02357-w.
LONARDO A, MANTOVANI A, PETTA S, et al. Metabolic mechanisms for and treatment of NAFLD or NASH occurring after liver transplantation[J]. Nat Rev Endocrinol, 2022, 18(10): 638-650. DOI: 10.1038/s41574-022-00711-5.
VERNUCCIO F, WHITNEY S A, RAVINDRA K, et al. CT and MR imaging evaluation of living liver donors[J]. Abdom Radiol (NY), 2021, 46(1): 17-28. DOI: 10.1007/s00261-019-02385-6.
DANIELLE B. Progress in liver transplantation, but better access is needed[J]. Liver Transplant, 2023, 29(4): 347-348. DOI: 10.1097/lvt.0000000000000081.
WANG Y X J, HUANG H, ZHENG C J, et al. Diffusion-weighted MRI of the liver: challenges and some solutions for the quantification of apparent diffusion coefficient and intravoxel incoherent motion[J]. Am J Nucl Med Mol Imaging, 2021, 11(2): 107-142.
CHUANG Y H, OU H Y, YU C Y, et al. Diffusion-weighted imaging for identifying patients at high risk of tumor recurrence following liver transplantation[J/OL]. Cancer Imag, 2019, 19(1): 74 [2022-07-13]. DOI: 10.1186/s40644-019-0264-y.
LEE S, KIM S H, HWANG J A, et al. Pre-operative ADC predicts early recurrence of HCC after curative resection[J]. Eur Radiol, 2019, 29(2): 1003-1012. DOI: 10.1007/s00330-018-5642-5.
NAKANISHI M, CHUMA M, HIGE S, et al. Relationship between diffusion-weighted magnetic resonance imaging and histological tumor grading of hepatocellular carcinoma[J]. Ann Surg Oncol, 2012, 19(4): 1302-1309. DOI: 10.1245/s10434-011-2066-8.
YAMADA S, MORINE Y, IKEMOTO T, et al. Impact of apparent diffusion coefficient on prognosis of early hepatocellular carcinoma: a case control study[J/OL]. BMC Surg, 2023, 23(1): 6 [2022-04-22]. DOI: 10.1186/s12893-022-01892-6.
CAO X, SHI H, DOU W Q, et al. Can DKI-MRI predict recurrence and invasion of peritumoral zone of hepatocellular carcinoma after transcatheter arterial chemoembolization?[J]. World J Gastrointest Surg, 2022, 14(10): 1150-1160. DOI: 10.4240/wjgs.v14.i10.1150.
WANG J, DOU W, SHI H, et al. Diffusion kurtosis imaging in liver: a preliminary reproducibility study in healthy volunteers[J]. MAGMA, 2020, 33(6): 877-883. DOI: 10.1007/s10334-020-00846-4.
LEVITSKY J, GOLDBERG D, SMITH A R, et al. Acute rejection increases risk of graft failure and death inRecent liver transplant recipients[J/OL]. Clin Gastroenterol Hepatol, 2017, 15(4): 584-593.e2 [2022-07-13]. DOI: 10.1016/j.cgh.2016.07.035.
LI C, HUANG L, YANG G Q, et al. Diagnostic value of MR diffusion kurtosis imaging in acute cellular rejection after liver transplantation[J]. Diagn Imag Interv Radiol, 2021, 30(1): 29-33. DOI: 10.3969/j.issn.1005-8001.2021.01.006.
PRASAD P V. Update on renal blood oxygenation level-dependent MRI to assess intrarenal oxygenation in chronic kidney disease[J]. Kidney Int, 2018, 93(4): 778-780. DOI: 10.1016/j.kint.2017.11.029.
CHIANG H J, CHOU M C, CHUANG Y H, et al. Correction: use of blood oxygen level-dependent magnetic resonance imaging to detect acute cellular rejection post-liver transplantation[J/OL]. Eur Radiol, 2023, 33(10): 7355 [2022-12-04]. DOI: 10.1007/s00330-023-09670-5.
CHIANG H J, CHANG W P, CHIANG H W, et al. Magnetic resonance spectroscopy in living-donor liver transplantation[J]. Transplant Proc, 2016, 48(4): 1003-1006. DOI: 10.1016/j.transproceed.2015.10.068.
CHENG Y, CHEN C, LAI C Y, et al. Assessment of donor fatty livers for liver transplantation. transplantation 2001; 71: 1221[J]. Transplantation, 2001, 71(9): 1206-1207. DOI: 10.1097/00007890-200105150-00003.
ZHENG D, GUO Z, SCHRODER P M, et al. Accuracy of MR imaging and MR spectroscopy for detection and quantification of hepatic steatosis in living liver donors: a meta-analysis[J]. Radiology, 2017, 282(1): 92-102. DOI: 10.1148/radiol.2016152571.
BURIAN M, HAJEK M, SEDIVY P, et al. Lipid profile and hepatic fat content measured by 1H MR spectroscopy in patients before and after liver transplantation[J/OL]. Metabolites, 2021, 11(9): 625 [2022-07-14]. DOI: 10.3390/metabo11090625.
BANNAS P, KRAMER H, HERNANDO D, et al. Quantitative magnetic resonance imaging of hepatic steatosis: validation in ex vivo human livers[J]. Hepatology, 2015, 62(5): 1444-1455. DOI: 10.1002/hep.28012.
CROOME KRISTOPHER P, LEE DAVID D, BURCIN T C. The "skinny" on assessment and utilization of steatotic liver grafts: a systematic review[J]. Liver Transplant, 2019, 25(3): 488-499. DOI: 10.1002/lt.25408.
VODKIN I, KUO A. Extended criteria donors in liver transplantation[J]. Clin Liver Dis, 2017, 21(2): 289-301. DOI: 10.1016/j.cld.2016.12.004.
QI Q, WEINSTOCK A K, CHUPETLOVSKA K, et al. Magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) is a viable alternative to liver biopsy for steatosis quantification in living liver donor transplantation[J/OL]. Clin Transplant, 2021, 35(7): e14339 [2022-07-08]. DOI: 10.1111/ctr.14339.
SATAPATHY S K, GONZALEZ H C, VANATTA J, et al. A pilot study of ex-vivo MRI-PDFF of donor livers for assessment of steatosis and predicting early graft dysfunction[J/OL]. PLoS One, 2020, 15(5): e0232006 [2022-07-15]. DOI: 10.1371/journal.pone.0232006.
HAMILTON G, SCHLEIN A N, WOLFSON T, et al. The relationship between liver triglyceride composition and proton density fat fraction as assessed by 1H MRS[J/OL]. NMR Biomed, 2020, 33(6): e4286 [2022-07-15]. DOI: 10.1002/nbm.4286.
IDILMAN I S, ANIKTAR H, IDILMAN R, et al. Hepatic steatosis: quantification by proton density fat fraction with MR imaging versus liver biopsy[J]. Radiology, 2013, 267(3): 767-775. DOI: 10.1148/radiol.13121360.
YOSHIZAWA E, YAMADA A. MRI-derived proton density fat fraction[J]. J Med Ultrasonics, 2021, 48(4): 497-506. DOI: 10.1007/s10396-021-01135-w.
OZTURK A, OLSON M C, SAMIR A E, et al. Liver fibrosis assessment: MR and US elastography[J]. Abdom Radiol, 2022, 47(9): 3037-3050. DOI: 10.1007/s00261-021-03269-4.
CATANIA R, LOPES VENDRAMI C, BOLSTER B D, et al. Intra-patient comparison of 3D and 2D magnetic resonance elastography techniques for assessment of liver stiffness[J]. Abdom Radiol (NY), 2022, 47(3): 998-1008. DOI: 10.1007/s00261-021-03355-7.
SANDRIN L, FOURQUET B, HASQUENOPH J M, et al. Transient elastography: a new noninvasive method for assessment of hepatic fibrosis[J]. Ultrasound Med Biol, 2003, 29(12): 1705-1713. DOI: 10.1016/j.ultrasmedbio.2003.07.001.
NAVIN P J, OLSON M C, KNUDSEN J M, et al. Elastography in the evaluation of liver allograft[J]. Abdom Radiol (NY), 2021, 46(1): 96-110. DOI: 10.1007/s00261-019-02400-w.
SINGH S, VENKATESH S K, KEAVENY A, et al. Diagnostic accuracy of magnetic resonance elastography in liver transplant recipients: a pooled analysis[J]. Ann Hepatol, 2016, 15(3): 363-376. DOI: 10.5604/16652681.1198808.
EL-METEINI M, SAKR M, ELDORRY A, et al. Non-invasive assessment of graft fibrosis after living donor liver transplantation: is there still a role for liver biopsy?[J]. Transplant Proc, 2019, 51(7): 2451-2456. DOI: 10.1016/j.transproceed.2019.01.197.
HARDING-THEOBALD E, LOUISSAINT J, MARAJ B, et al. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma[J]. Aliment Pharmacol Ther, 2021, 54(7): 890-901. DOI: 10.1111/apt.16563.
CARBONELL G, KENNEDY P, BANE O, et al. Precision of MRI radiomics features in the liver and hepatocellular carcinoma[J]. Eur Radiol, 2022, 32(3): 2030-2040. DOI: 10.1007/s00330-021-08282-1.
SUNG Y S, PARK B, PARK H J, et al. Radiomics and deep learning in liver diseases[J]. J Gastroenterol Hepatol, 2021, 36(3): 561-568. DOI: 10.1111/jgh.15414.
HE T, FONG J N, MOORE L W, et al. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer[J/OL]. Comput Med Imaging Graph, 2021, 89: 101894 [2022-07-22]. DOI: 10.1016/j.compmedimag.2021.101894.
HU W M, YANG H Y, MAO Y L. The application of imageology based on artificial intelligence in liver diseases[J]. Chin J Gen Surg, 2019, 34(7): 646-648. DOI: 10.3760/cma.j.issn.1007-631X.2019.07.033.
GUO D, GU D, WANG H, et al. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation[J]. Eur J Radiol, 2019, 117: 33-40. DOI: 10.1016/j.ejrad.2019.05.010.
IVANICS T, NELSON W, PATEL M S, et al. The Toronto postliver transplantation hepatocellular carcinoma recurrence calculator: a machine learning approach[J]. Liver Transpl, 2021, 28(4): 593-602. DOI: 10.1002/lt.26332.
ZHAO J W, CHEN X X, GUO X D, et al. Prediction of early recurrence of hepatocellular carcinoma after liver transplantation based on CT radiomics model[J]. Med J Chin People's Armed Police Force, 2021, 32(5): 399-402, 406. DOI: 10.14010/j.cnki.wjyx.2021.05.008.
ZHAO J W, SHU X, CHEN X X, et al. Prediction of early recurrence of hepatocellular carcinoma after liver transplantation based on computed tomography radiomics nomogram[J]. Hepatobiliary Pancreat Dis Int, 2022, 21(6): 543-550. DOI: 10.1016/j.hbpd.2022.05.013.
MAZZAFERRO V, REGALIA E, DOCI R, et al. Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis[J]. N Engl J Med, 1996, 334(11): 693-699. DOI: 10.1056/nejm199603143341104.
VICTOR D W, MONSOUR H P, BOKTOUR M, et al. Outcomes of liver transplantation for hepatocellular carcinoma beyond the university of California San francisco criteria: a single-center experience[J]. Transplantation, 2020, 104(1): 113-121. DOI: 10.1097/tp.0000000000002835.
CHU M J J, DARE A J, PHILLIPS A R J, et al. Donor hepatic steatosis and outcome after liver transplantation: a systematic review[J]. J Gastrointest Surg, 2015, 19(9): 1713-1724. DOI: 10.1007/s11605-015-2832-1.
MIKOLASEVIC I, FILIPEC-KANIZAJ T, MIJIC M, et al. Nonalcoholic fatty liver disease and liver transplantation-Where do we stand?[J]. World J Gastroenterol, 2018, 24(14): 1491-1506. DOI: 10.3748/wjg.v24.i14.1491.
FLECHTENMACHER C, SCHIRMACHER P, SCHEMMER P. Donor liver histology—a valuable tool in graft selection[J]. Langenbecks Arch Surg, 2015, 400(5): 551-557. DOI: 10.1007/s00423-015-1298-7.
POLLOK J M, TINGUELY P, BERENGUER M, et al. Enhanced recovery for liver transplantation: recommendations from the 2022 International Liver Transplantation Society consensus conference[J]. Lancet Gastroenterol Hepatol, 2023, 8(1): 81-94. DOI: 10.1016/s2468-1253(22)00268-0.
DING S, YANG W, SUN X, et al. Computed tomography-based radiomic analysis for preoperatively predicting the macrovesicular steatosis grade in cadaveric donor liver transplantation[J/OL]. Biomed Res Int, 2022, 2022: 2491023 [2023-01-05]. DOI: 10.1155/2022/2491023.
CHEN G, JIANG J P, WANG X Q, et al. Evaluation of hepatic steatosis before liver transplantation in ex vivo by volumetric quantitative PDFF-MRI[J]. Magn Reson Med, 2021, 85(5): 2805-2814. DOI: 10.1002/mrm.28592.

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