MRI Features for Predicting Microvascular Invasion of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis
Seung Baek Hong,
Sang Hyun Choi,
So Yeon Kim,
Ju Hyun Shim,
Seung Soo Lee,
Jae Ho Byun,
Seong Ho Park,
Kyung Won Kim,
Suk Kim,
Nam Kyung Lee
Affiliations
Seung Baek Hong
Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
Sang Hyun Choi
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
So Yeon Kim
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
Ju Hyun Shim
Department of Gastroenterology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
Seung Soo Lee
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
Jae Ho Byun
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
Seong Ho Park
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
Kyung Won Kim
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
Suk Kim
Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
Nam Kyung Lee
Department of Radiology, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
Purpose: Microvascular invasion (MVI) is an important prognostic factor in patients with hepatocellular carcinoma (HCC). However, the reported results of magnetic resonance imaging (MRI) features for predicting MVI of HCC are variable and conflicting. Therefore, this meta-analysis aimed to identify the significant MRI features for MVI of HCC and to determine their diagnostic value. Methods: Original studies reporting the diagnostic performance of MRI for predicting MVI of HCC were identified in MEDLINE and EMBASE up until January 15, 2020. Study quality was assessed using QUADAS-2. A bivariate random-effects model was used to calculate the meta-analytic pooled diagnostic odds ratio (DOR) and 95% confidence interval (CI) for each MRI feature for diagnosing MVI in HCC. The meta-analytic pooled sensitivity and specificity were calculated for the significant MRI features. Results: Among 235 screened articles, we found 36 studies including 4,274 HCCs. Of the 15 available MRI features, 7 were significantly associated with MVI: larger tumor size (>5 cm) (DOR = 5.2, 95% CI [3.0–9.0]), rim arterial enhancement (4.2, 95% CI [1.7–10.6]), arterial peritumoral enhancement (4.4, 95% CI [2.8–6.9]), peritumoral hypointensity on hepatobiliary phase imaging (HBP) (8.2, 95% CI [4.4–15.2]), nonsmooth tumor margin (3.2, 95% CI [2.2–4.4]), multifocality (7.1, 95% CI [2.6–19.5]), and hypointensity on T1-weighted imaging (T1WI) (4.9, 95% CI [2.5–9.6]). Both peritumoral hypointensity on HBP and multifocality showed very high meta-analytic pooled specificities for diagnosing MVI (91.1% [85.4–94.8%] and 93.3% [74.5–98.5%], respectively). Conclusions: Seven MRI features including larger tumor size, rim arterial enhancement, arterial peritumoral enhancement, peritumoral hypointensity on HBP, nonsmooth margin, multifocality, and hypointensity on T1WI were significant predictors for MVI of HCC. These MRI features predictive of MVI can be useful in the management of HCC.
