Completed Trial: AI Algorithm vs LI-RADS for Hepatocellular Carcinoma Detection on CT — Validation Results Pending
247 Health News Editorial
·22 May 2026
·3 min read
·clinical-trialsA completed Hong Kong trial validated an AI algorithm against LI-RADS criteria for hepatocellular carcinoma detection on CT in 300 at-risk patients. Results pending publication could impact liver cancer screening protocols globally.
Originally reported by ClinicalTrials.gov
References
- 1.Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68(6), 394-424. https://doi.org/10.3322/caac.21492
- 2.Chernyak, V., Fowler, K. J., Kamaya, A., Kielar, A. Z., Elsayes, K. M., Bashir, M. R., ... & Tang, A. (2018). Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology, 289(3), 738-749. https://doi.org/10.1148/radiol.2018181494
- 3.European Association for the Study of the Liver. (2018). EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. Journal of Hepatology, 69(1), 182-236. https://doi.org/10.1016/j.jhep.2018.03.019
- 4.Hamm, C. A., Wang, C. J., Savic, L. J., Ferrante, M., Schobert, I., Schlachter, T., ... & Chapiro, J. (2019). Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. European Radiology, 29(7), 3714-3725. https://doi.org/10.1007/s00330-019-06205-9
- 5.Kielar, A. Z., Chernyak, V., Bashir, M. R., Do, R. K., Fowler, K. J., Mitchell, D. G., ... & Tang, A. (2018). LI-RADS 2017: coming of age. Journal of Magnetic Resonance Imaging, 47(2), 336-342. https://doi.org/10.1002/jmri.25998
- 6.Liu, F., Liu, D., Wang, K., Xie, X., Su, L., Kuang, M., ... & Chen, S. (2019). Deep learning radiomics based on contrast-enhanced ultrasound might optimize curative treatments for very-early or early-stage hepatocellular carcinoma patients. Liver Cancer, 8(6), 480-492. https://doi.org/10.1159/000501374
- 7.Marrero, J. A., Kulik, L. M., Sirlin, C. B., Zhu, A. X., Finn, R. S., Abecassis, M. M., ... & Heimbach, J. K. (2018). Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology, 68(2), 723-750. https://doi.org/10.1002/hep.29913
- 8.Singal, A. G., Lampertico, P., & Nahon, P. (2020). Epidemiology and surveillance for hepatocellular carcinoma: new trends. Journal of Hepatology, 72(2), 250-261. https://doi.org/10.1016/j.jhep.2019.08.025
- 9.University of Hong Kong. (2024). A Prototype Artificial Intelligence Algorithm Versus Liver Imaging Reporting and Data System (LI-RADS) Criteria in Diagnosing Hepatocellular Carcinoma on Computed Tomography: a Randomized Trial. ClinicalTrials.gov. NCT06626087. https://clinicaltrials.gov/study/NCT06626087
- 10.Yasaka, K., Akai, H., Abe, O., & Kiryu, S. (2018). Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology, 286(3), 887-896. https://doi.org/10.1148/radiol.2017170706
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