ArticleFrontiers in oncology2026
Artificial intelligence-assisted rapid on-site evaluation in liver biopsy: a diagnostic accuracy study.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Liver biopsy is the gold standard for diagnosing liver lesions but is hampered by the time-consuming nature of conventional pathology. Artificial Intelligence-assisted Rapid On-Site Evaluation (AI-ROSE) offers a promising solution for real-time assessment of biopsy samples. This study aims to evaluate the diagnostic performance of AI-ROSE in liver biopsies using histopathology as the reference standard. Methods: Fifty-eight patients with liver lesions undergoing CT-guided percutaneous biopsy were prospectively enrolled. Each biopsy sample underwent triple assessment: AI-ROSE analysis, exfoliative cytology, and histopathological examination. Diagnostic sensitivity, specificity, accuracy, and predictive values were calculated. Serial (both tests positive) and parallel (either test positive) combinations of AI-ROSE and cytology were also analyzed. The agreement between methods was assessed using the Kappa statistic. Results: AI-ROSE showed a sensitivity of 92.31% (95% CI: 81.8% - 97.1%) and an accuracy of 87.93% (95% CI: 77.2% - 94.2%), outperforming exfoliative cytology (sensitivity: 86.54%; accuracy: 84.48%). The parallel application of AI-ROSE and cytology achieved the highest sensitivity of 96.15% (95% CI: 87.02-100.00) and significantly improved agreement with the gold standard (Kappa = 0.498, p = 0.0015). Conclusion: This study shows AI-ROSE not only has better sensitivity and accuracy in single-item diagnosis than exfoliative cytology in liver biopsy, but demonstrates excellent synergistic value in parallel application, which can greatly improve the reliability of intraoperative diagnosis. This indicates AI-ROSE has important application prospects in optimizing the clinical decision-making process and reducing the risk of intraoperative missed diagnosis.
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