Aim: To evaluate the overall diagnostic performance of magnetic resonance imaging (MRI), different image features, and different image analysis methods in predicting hepatocellular carcinoma (HCC) with cytokeratin 19 (CK19) expression.
Materials and methods: A systematic literature search was performed to identify studies using MRI to predict HCC with CK19 expression between 2012 and 2023. Data were extracted to calculate the pooled sensitivity and specificity. Overall diagnostic performance was assessed using areas under the summary receiver operating characteristic curve (AUC). Subgroup analyses were conducted for specific image features and according to image analysis methods (traditional image feature, radiomics, and combined methods). Z-test statistics was used to analyse the differences in diagnostic performance between combined and individual methods.
Results: Eleven studies with 14 datasets (1,278 lesions from 1,264 patients) were included. The overall pooled sensitivity, specificity, and AUC with corresponding 95% confidence intervals were estimated to be 0.72 (0.55, 0.85), 0.88 (0.80, 0.93), and 0.89 (0.86, 0.91) for MRI in predicting HCC with CK19 expression. Combined methods had higher sensitivity than image feature methods (0.86 versus 0.54, p=0.001), with no difference in specificity (0.85 versus 0.87, p=0.641). There were no significant differences between radiomics and combined methods regarding sensitivity (p=0.796) and specificity (p=0.535), respectively.
Conclusion: MRI shows moderate sensitivity and high specificity in identifying HCC with CK19 expression. The application of radiomics can improve the sensitivity of MRI in identifying HCC with CK19 expression.
Copyright © 2023. Published by Elsevier Ltd.