A Systematic Review and Meta-Analysis of MRI Radiomics for PredictingMicrovascular Invasion in Patients with Hepatocellular Carcinoma

医学 无线电技术 肝细胞癌 荟萃分析 接收机工作特性 子群分析 科克伦图书馆 内科学 肿瘤科 放射科 曲线下面积 磁共振成像
作者
H. Zhou,Jin-mei Cheng,Tian‐wu Chen,Xiaoming Zhang,Jing Ou,Jinming Cao,Hong-jun Li
出处
期刊:Current Medical Imaging Reviews [Bentham Science]
卷期号:20: e15734056256824-e15734056256824 被引量:4
标识
DOI:10.2174/0115734056256824231204073534
摘要

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.
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