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

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

You might also read

Related Articles

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

Sort by
Same author

AI-assisted extraction of opportunistic findings from oncologic CT reports using large language models.

Scientific reports·2026
Same author

A Two-Stage Pipeline for Linking Clinical Notes to SNOMED CT.

Studies in health technology and informatics·2026
Same author

A call for action to strengthen stakeholder readiness for ICF data exchange in European health data space: a structured narrative review.

Frontiers in public health·2026
Same author

Integrating Confidence, Difficulty, and Language Model Calibration for Better Explainability in Clinical Documents Coding: Applications of AI.

JMIR AI·2026
Same author

SNOMED CT entity linking challenge.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

Graph Neural Networks for Gleason Grading in Prostate Histopathology Images.

Studies in health technology and informatics·2025
Same journal

The Essential Components and Critical Conditions for Success in a Learning Health System in Oncology.

Studies in health technology and informatics·2026
Same journal

Use of Artificial Intelligence in Screening for Adolescent Idiopathic Scoliosis: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Movement Related Biomechanics in Adolescent Idiopathic Scoliosis: A Review of Reviews.

Studies in health technology and informatics·2026
Same journal

The Impact of Surgical Correction of Adolescent Idiopathic Scoliosis Using Posterior Spinal Fusion on Selected Radiological Parameters and Respiratory Function.

Studies in health technology and informatics·2026
Same journal

Acute Effect of Physio-logic® Exercises on Muscle Tone and Stiffness in Adolescent Idiopathic Scoliosis Patients: A Preliminary Study.

Studies in health technology and informatics·2026
Same journal

Effects of Integrated Music and Occupational Therapy on Motor and Autonomic Function in Children with Neurogenic Scoliosis.

Studies in health technology and informatics·2026
See all related articles

Related Experiment Video

Updated: May 24, 2026

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

Multimodal Graph-Based Model for Discrete-Time Survival Prediction in Liver Cancer.

Hafsa Akebli1, Vincenzo Della Mea1, Kevin Roitero1

  • 1University of Udine, Italy.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a multimodal framework for predicting liver cancer survival using histology and clinical data. The combined approach significantly improves prognostic accuracy for hepatocellular carcinoma (HCC).

Keywords:
Graph Neural NetworksHistopathologyLiver CancerMultimodalSurvival Prediction

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: May 24, 2026

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

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Oncology
  • Medical Informatics
  • Digital Pathology

Background:

  • Hepatocellular carcinoma (HCC) is a major cause of cancer death, necessitating precise survival prediction for patient management.
  • Accurate prognosis in HCC is crucial for tailoring treatment strategies and improving patient outcomes.

Purpose of the Study:

  • To develop and validate a multimodal framework for discrete-time survival prediction in HCC.
  • To integrate histopathological and clinical data for enhanced prognostic accuracy.

Main Methods:

  • Utilized Whole-Slide Images (WSIs) and clinical data from the TCGA-LIHC cohort.
  • Extracted patch-level features using the UNI2-h foundation model and aggregated them with a Graph Attention Network.
  • Encoded clinical variables with a sentence transformer and fused embeddings using intermediate strategies.
  • Employed a multi-layer perceptron with negative discrete-time log-likelihood loss for survival prediction, handling censored data.

Main Results:

  • The multimodal framework demonstrated superior performance compared to unimodal approaches.
  • Concatenation-based fusion achieved the highest performance, with an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.818.
  • The model accurately predicted survival within the [0, 1) and [1, 5] year intervals.

Conclusions:

  • Histopathological and clinical data offer complementary prognostic information for HCC.
  • The proposed multimodal framework provides a robust and interpretable method for HCC survival modeling.
  • This approach can aid in guiding prognosis and treatment decisions for liver cancer patients.