Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis

Simple Summary In this study, after conducting a comprehensive review of 1356 papers that evaluated the diagnostic performance of deep learning (DL) methods based on medical images for hepatocellular carcinoma (HCC), the findings showed a pooled sensitivity of 89% (95% CI: 87-91), a specificity of 90% (95% CI: 87-92), and an AUC of 0.95 (95% CI: 0.93-0.97). In addition, both the DL methods and human clinicians demonstrated similar levels of performance in HCC detection, with receiver operating characteristic curve (ROC) values of 0.97 (95% CI: 0.95-0.98) for both groups, indicating no discernible difference. Although the heterogeneity was obvious, the utilization of DL methods for diagnosing HCC through medical images has shown promising outcomes.Abstract (1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87-91), the specificity was 90% (95% CI: 87-92), and the AUC was 0.95 (95% CI: 0.93-0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field.

Automated summary

This study comprehensively reviewed 1356 papers on the diagnostic performance of deep learning methods in hepatocellular carcinoma (HCC) based on medical images. The findings showed that deep learning methods demonstrated high sensitivity, specificity, and accuracy in HCC diagnosis, similar to human clinicians.