Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Patterns of complementary and alternative medicine use and factors influencing its utilization in thyroid cancer: findings from the MASTER study.

BMC complementary medicine and therapies·2026
Same author

KASL clinical practice guidelines for management of chronic hepatitis B Endorsed by the East Asia Liver Alliance (EALA).

Clinical and molecular hepatology·2026
Same author

Risk of Intrahepatic and Extrahepatic Cancers in Hepatitis C Virus Infection: A Nationwide Cohort Study in Korea, 2005-2023.

Liver international : official journal of the International Association for the Study of the Liver·2026
Same author

Efficacy and Safety of Switching from Entecavir to Tenofovir Alafenamide in Chronic Hepatitis B: A Multicenter Randomized Trial in Korea.

Gut and liver·2026
Same author

Second-Line and Subsequent Therapies after Atezolizumab Plus Bevacizumab Treatment in Hepatocellular Carcinoma: A Multicenter Prospective Cohort Study.

Gut and liver·2026
Same author

Enhanced Anticancer Activity of Ibuprofen and 2'-Hydroxy-2,3,5'-trimethoxychalcone-Linked Polymeric Micelles in HeLa Cells.

ACS omega·2026

Related Experiment Video

Updated: Dec 7, 2025

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth
08:15

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth

Published on: February 2, 2024

1.2K

Novel Model to Predict HCC Recurrence after Liver Transplantation Obtained Using Deep Learning: A Multicenter Study.

Joon Yeul Nam1, Jeong-Hoon Lee1, Junho Bae2

  • 1Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Korea.

Cancers
|October 2, 2020
PubMed
Summary

A new artificial intelligence model, MoRAL-AI, accurately predicts hepatocellular carcinoma recurrence after liver transplantation, outperforming existing criteria for better patient outcomes.

Keywords:
Milan criteriadeep learninghepatocellular carcinomaliver transplantation

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

398

Related Experiment Videos

Last Updated: Dec 7, 2025

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth
08:15

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth

Published on: February 2, 2024

1.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

398

Area of Science:

  • Hepatology
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Conventional regression models have limitations in extending liver transplantation (LT) criteria for hepatocellular carcinoma (HCC).
  • Accurate prediction of tumor recurrence post-LT is crucial for patient management and improving outcomes.

Purpose of the Study:

  • To develop and validate a novel artificial intelligence (AI) model, MoRAL-AI, for predicting tumor recurrence after LT in HCC patients.
  • To compare the performance of MoRAL-AI against established criteria for HCC in LT.

Main Methods:

  • A residual block-based deep neural network was utilized to derive the MoRAL-AI model.
  • The study included 563 patients undergoing LT for HCC, with independent derivation (n=349) and validation (n=214) cohorts.
  • Performance was evaluated using discrimination function (c-index) and compared against Milan, MoRAL, UCSF, up-to-seven, and Kyoto criteria.

Main Results:

  • MoRAL-AI demonstrated superior prediction of tumor recurrence in the validation cohort (c-index = 0.75) compared to all conventional criteria.
  • Key predictive parameters for MoRAL-AI included tumor diameter, alpha-fetoprotein, age, and protein induced by vitamin K absence-II.
  • The model showed significantly better discrimination than Milan (c-index=0.64) and other criteria (p < 0.001).

Conclusions:

  • MoRAL-AI offers enhanced predictability for HCC recurrence post-LT, surpassing current clinical criteria.
  • The AI model's ability to evolve with additional data suggests potential for continuous improvement in predicting LT outcomes.