Accuracy of artificial intelligence models integrating machine learning and deep learning in detecting microvascular invasion in liver cancer: A systematic review and meta-analysis.

Abstract

e16184 Background: Hepatocellular carcinoma (HCC) is a global health challenge, ranking sixth in incidence and third in cancer-related mortality. Microvascular invasion (MVI) is a crucial prognostic marker influencing recurrence rates and survival. Accurate preoperative MVI detection can guide surgical planning but is limited by invasive histopathological exams and interobserver variability. This study evaluates the diagnostic performance of artificial intelligence (AI) models, including machine learning (ML) and deep learning (DL), in predicting MVI in HCC using imaging modalities such as CT, MRI, ultrasound, PET/CT, and histopathology. Methods: A systematic review and meta-analysis followed PRISMA 2020 guidelines, covering studies from 2010 to 2023. Comprehensive searches were conducted across PubMed, Scopus, Web of Science, Embase, Cochrane Library, Google Scholar, European PMC, and BioMed Central. AI models were assessed for diagnostic accuracy using QUADAS-2 and Radiomics Quality Score (RQS). Metrics like the area under the curve (AUC), sensitivity, and specificity were analyzed. Results: This meta-analysis synthesized data from 51 studies, encompassing 6,257 records. DL models showed a pooled AUC of 0.84 (95% CI: 0.80–0.86), with sensitivity and specificity of 0.79 (95% CI: 0.75–0.82) and 0.84 (95% CI: 0.79–0.88), respectively. ML models achieved a pooled AUC of 0.83 (95% CI: 0.80–0.86), sensitivity of 0.79 (95% CI: 0.71–0.85), and higher specificity of 0.88 (95% CI: 0.84–0.92). Across imaging modalities, MRI and CT-based models achieved pooled AUCs of 0.87 and 0.83 for DL and 0.85 and 0.82 for ML, respectively. Ultrasound-based models demonstrated higher specificity but slightly lower sensitivity. Models incorporating clinical features did not outperform purely radiomics-based approaches. Quality assessments revealed low bias risks in patient selection (88%), index tests (94%), and reference standards (98%). However, only 51% of studies addressed inter-scanner variability, and 55% incorporated calibration or resampling. The mean RQS was 40%, with 84% adhering to robust imaging protocols. Conclusions: AI models, particularly DL, exhibit robust accuracy in predicting MVI in HCC, showing promise for integration into clinical workflows. These tools could enable personalized preoperative planning, improving patient outcomes and reducing recurrence risks. Standardized protocols, prospective validation, and broader adoption of advanced AI methods are needed to ensure consistent clinical utility and cost-effectiveness.