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 Experiment Video

Updated: Jun 13, 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

Predicting Post-Radiotherapy Epigenetic Age Acceleration From Pre-Treatment Data Using a Machine Learning Framework

Runze Yan1, Guanlin Dai1, Yufen Lin1,2

  • 1Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, USA.

Cancer Medicine
|June 12, 2026
PubMed
Summary

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

Efficacy and Modulating Factors of Repetitive Transcranial Magnetic Stimulation on Motor Function in Parkinson's Disease: A Meta-Analysis.

Physiotherapy research international : the journal for researchers and clinicians in physical therapy·2026
Same author

Multimodal neuroimaging elucidates the posterior-to-anterior gradient of nigrostriatal degeneration in Parkinson's disease.

NPJ Parkinson's disease·2026
Same author

Monocyte abundance and glycolytic reprogramming associate with motivational impairment in depression.

Brain, behavior, and immunity·2026
Same author

Knowledge Graph Augmented Large Language Models for Disease Prediction.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Clinical Evaluation of the Revised Biological and Clinical Staging Criteria for Alzheimer Disease in China.

Neurology·2026
This summary is machine-generated.

Machine learning predicts epigenetic age acceleration (EAA) in head and neck cancer patients undergoing radiotherapy. This approach uses pre-treatment data to identify high-risk individuals early, improving treatment planning and outcomes.

Area of Science:

  • Oncology
  • Genetics
  • Computational Biology

Background:

  • Epigenetic age acceleration (EAA) is linked to adverse outcomes in head and neck cancer (HNC) patients post-radiotherapy.
  • Predicting EAA before or during treatment is crucial for clinical decision-making but current methods are limited.
  • Existing methods often require expensive epigenetic assays and cannot effectively forecast EAA changes throughout treatment.

Purpose of the Study:

  • To develop and validate a machine learning (ML) framework for predicting EAA in HNC patients following radiotherapy.
  • To forecast EAA trajectories at multiple post-treatment time points using pre-treatment data.
  • To enable cost-effective early identification of patients at high risk for adverse outcomes.

Main Methods:

  • Utilized pre-radiotherapy sociodemographic data, symptom reports, clinical measurements, and immune biomarkers.
Keywords:
deep learningepigenetic age accelerationhead and neck cancerlongitudinal predictionmachine learningradiotherapy

Related Experiment Videos

Last Updated: Jun 13, 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

  • Developed and explored various ML models, including deep learning (TabNet), to predict EAA.
  • Validated predictions across three post-treatment stages: immediately, 6 months, and 12 months post-radiotherapy.

    • Main Results:

    • Deep learning models, specifically TabNet, demonstrated superior performance (average RMSE: 4.08) compared to conventional algorithms.
    • Prediction accuracy was highest immediately post-treatment (Time 2 RMSE: 4.87).
    • Baseline immune markers (absolute eosinophil count, hemoglobin) were consistent predictors, and prediction accuracy varied across patient subgroups (RMSE range: 1.70-4.34).

    Conclusions:

    • Pre-treatment demographic and clinical data can effectively predict post-treatment EAA trajectories.
    • The developed ML framework offers a cost-effective alternative to expensive epigenetic assays for early risk identification.
    • This approach facilitates timely interventions for high-risk HNC patients before adverse effects manifest.