Title:A Systematic Review and Meta-Analysis of MRI Radiomics for Predicting
Microvascular Invasion in Patients with Hepatocellular Carcinoma
Volume: 20
Author(s): Hai-ying Zhou, Jin-mei Cheng, Tian-wu Chen*, Xiao-ming Zhang, Jing Ou, Jin-ming Cao and Hong-jun Li*
Affiliation:
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong
637000, Sichuan, China
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, Beijing 100069, China
Keywords:
Radiomics, Microvascular invasion, Hepatocellular carcinoma, Magnetic resonance imaging, Systematic review, Meta-analysis.
Abstract:
Background:
The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain.
Objective:
To investigate the prediction performance of MRI radiomics for MVI in HCC.
Methods:
Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases
of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated.
Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction
performance of MRI radiomics were identified by subgroup analyses.
Results:
13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or
Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to
17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 – 0.86), specificity of 0.79
(95%CI: 0.76 – 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 – 0.91) to predict MVI in
HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC:
0.90 vs 0.85, P < 0.05).
Conclusion:
MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed
to improve the reliability and reproducibility of the radiomics models for clinical application.
The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No.
CRD42022333822).
