» Articles » PMID: 36062283

Diagnostic Accuracy of the Apparent Diffusion Coefficient for Microvascular Invasion in Hepatocellular Carcinoma: A Meta-analysis

Overview
Specialty Gastroenterology
Date 2022 Sep 5
PMID 36062283
Authors
Affiliations
Soon will be listed here.
Abstract

Background And Aims: Microvascular invasion (MVI) is a major risk factor for the early recurrence of hepatocellular carcinoma (HCC) and it seriously worsens the prognosis. Accurate preoperative evaluation of the presence of MVI could greatly benefit the treatment management and prognosis prediction of HCC patients. The study aim was to evaluate the diagnostic performance of the apparent diffusion coefficient (ADC), a quantitative parameter for the preoperative diagnosis MVI in HCC patients.

Methods: Original articles about diffusion-weighted imaging (DWI) and/or intravoxel incoherent motion (IVIM) conducted on a 3.0 or 1.5 Tesla magnetic resonance imaging (MRI) system indexed through January 17, 2021were collected from MEDLINE/PubMed, Web of Science, EMBASE, and the Cochrane Library. Methodological quality was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The pooled sensitivity, specificity, and summary area under the receiver operating characteristic curve (AUROC) were calculated, and meta-regression analysis was performed using a bivariate random effects model through a meta-analysis.

Results: Nine original articles with a total of 988 HCCs were included. Most studies had low bias risk and minimal applicability concerns. The pooled sensitivity, specificity and AUROC of the ADC value were 73%, 70%, and 0.78, respectively. The time interval between the index test and the reference standard was identified as a possible source of heterogeneity by subgroup meta-regression analysis.

Conclusions: Meta-analysis showed that the ADC value had moderate accuracy for predicting MVI in HCC. The time interval accounted for the heterogeneity.

Citing Articles

Preoperative prediction of microvascular invasion: new insights into personalized therapy for early-stage hepatocellular carcinoma.

Wang F, Liao H, Chen X, Lei H, Luo G, Chen G Quant Imaging Med Surg. 2024; 14(7):5205-5223.

PMID: 39022260 PMC: 11250313. DOI: 10.21037/qims-24-44.


Detecting microvascular invasion in hepatocellular carcinoma using the impeded diffusion fraction technique to sense macromolecular coordinated water.

Zhang Y, Sheng R, Yang C, Dai Y, Zeng M Abdom Radiol (NY). 2024; 49(6):1892-1904.

PMID: 38526597 DOI: 10.1007/s00261-024-04230-x.


Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy.

Li J, Ma Y, Yang C, Qiu G, Chen J, Tan X Front Oncol. 2024; 14:1277698.

PMID: 38463221 PMC: 10920317. DOI: 10.3389/fonc.2024.1277698.


Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study.

Deng Y, Yang D, Tan X, Xu H, Xu L, Ren A BMC Med Imaging. 2024; 24(1):29.

PMID: 38281008 PMC: 10821254. DOI: 10.1186/s12880-024-01206-7.

References
1.
Taouli B, Koh D . Diffusion-weighted MR imaging of the liver. Radiology. 2009; 254(1):47-66. DOI: 10.1148/radiol.09090021. View

2.
Xu P, Zeng M, Liu K, Shan Y, Xu C, Lin J . Microvascular invasion in small hepatocellular carcinoma: is it predictable with preoperative diffusion-weighted imaging?. J Gastroenterol Hepatol. 2013; 29(2):330-6. DOI: 10.1111/jgh.12358. View

3.
Feng S, Jia Y, Liao B, Huang B, Zhou Q, Li X . Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI. Eur Radiol. 2019; 29(9):4648-4659. DOI: 10.1007/s00330-018-5935-8. View

4.
Golabi P, Fazel S, Otgonsuren M, Sayiner M, Locklear C, Younossi Z . Mortality assessment of patients with hepatocellular carcinoma according to underlying disease and treatment modalities. Medicine (Baltimore). 2017; 96(9):e5904. PMC: 5340426. DOI: 10.1097/MD.0000000000005904. View

5.
Vogelgesang F, Schlattmann P, Dewey M . The Evaluation of Bivariate Mixed Models in Meta-analyses of Diagnostic Accuracy Studies with SAS, Stata and R. Methods Inf Med. 2018; 57(3):111-119. DOI: 10.3414/ME17-01-0021. View