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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

4.9K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Nutritional-inflammatory indices optimize the diagnostic performance of FIB-4 for advanced fibrosis/cirrhosis in patients with benign liver disease.

Annals of medicine·2026
Same author

Neuroprotective Effects of Ginsenoside Rg3 in Depressed Mice via Inhibition of the C1q Complement Pathway.

CNS neuroscience & therapeutics·2025
Same author

[Protective Effect of Ginsenoside Rg3 on Lipopolysaccharide-Induced Neuronal-Galial Interaction Injury Model].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2025
Same author

β-arrestin2 is indispensable for the antidepressant effects of fluoxetine via inhibiting astrocytic pyroptosis in chronic mild stress mouse model for depression.

European journal of pharmacology·2024
Same author

Diagnostic Value of OPNI in Hepatocellular Carcinoma.

Oncology·2023
Same author

Prognostic Significance of Hemoglobin, Albumin, Lymphocyte and Platelet (HALP) Score in Hepatocellular Carcinoma.

Journal of hepatocellular carcinoma·2023

Related Experiment Video

Updated: May 30, 2025

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

8.2K

Magnetic resonance imaging radiomics based on artificial intelligence is helpful to evaluate the prognosis of single

Jing Zhou1, Daofeng Yang1, Hao Tang2

  • 1Department of Infectious Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Heliyon
|January 27, 2025
PubMed
Summary

This study developed an AI-powered model using magnetic resonance imaging features to predict outcomes for hepatocellular carcinoma (HCC) patients. The integrated nomogram effectively forecasts recurrence-free and overall survival, aiding in risk stratification.

Keywords:
Artificial intelligenceHepatocellular carcinomaPrognosis

More Related Videos

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.1K
Non-Invasive PET/MR Imaging in an Orthotopic Mouse Model of Hepatocellular Carcinoma
07:47

Non-Invasive PET/MR Imaging in an Orthotopic Mouse Model of Hepatocellular Carcinoma

Published on: August 31, 2022

2.2K

Related Experiment Videos

Last Updated: May 30, 2025

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

8.2K
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.1K
Non-Invasive PET/MR Imaging in an Orthotopic Mouse Model of Hepatocellular Carcinoma
07:47

Non-Invasive PET/MR Imaging in an Orthotopic Mouse Model of Hepatocellular Carcinoma

Published on: August 31, 2022

2.2K

Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Hepatocellular carcinoma (HCC) prognosis prediction traditionally relies on single-feature models.
  • There is a need for comprehensive models to identify HCC patients with poor prognosis.
  • Artificial intelligence (AI) offers advanced capabilities for feature extraction and predictive modeling in oncology.

Purpose of the Study:

  • To develop and validate a comprehensive AI-based prediction model for identifying patients with poor prognosis in single hepatocellular carcinoma (HCC).
  • To integrate diverse data types, including clinical information and magnetic resonance (MR) imaging features, for enhanced predictive accuracy.
  • To assess the model's ability to predict postoperative recurrence and stratify overall survival (OS) risk in HCC patients.

Main Methods:

  • Utilized AI to extract features from magnetic resonance (MR) images of 236 single HCC patients.
  • Developed and compared prediction models including linear regression (LR), radiomics, and deep transfer learning (DTL) using a light gradient-boosting machine (Light GBM) algorithm.
  • Constructed an integrated nomogram combining key predictive signatures.

Main Results:

  • The DTL model (AUC: 0.784) and radiomics model (AUC: 0.761) outperformed the clinical LR model (AUC: 0.658).
  • The integrated nomogram demonstrated the highest predictive performance (largest AUC) across all models.
  • The integrated nomogram accurately predicted recurrence-free survival (RFS) and overall survival (OS) in both training (C-index: 0.735 and 0.712) and test cohorts (C-index: 0.718 and 0.740).

Conclusions:

  • An integrated nomogram incorporating AI-extracted MR imaging features provides robust prediction of postoperative recurrence in single HCC patients.
  • This comprehensive model effectively stratifies the risk of overall survival (OS) after surgery for HCC.
  • The developed AI-driven approach enhances prognostic accuracy and clinical decision-making for HCC management.