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

Determining Optimal Fractionation of Neoadjuvant Radiation in Low-Risk, Early-Stage Breast Cancer-Randomized SIGNAL Clinical Trial.

Cancers·2026
Same author

Evaluating HER2 Scoring Criteria in Endometrial Carcinoma: Gynecologic Versus Gastric Guidelines for Trastuzumab and Trastuzumab-Deruxtecan Selection.

Cancers·2026
Same author

Essential embryology for the Canadian pathologists' assistant.

Anatomical sciences education·2026
Same author

Efficacy and safety of N-acetylcysteine in patients with mild cognitive impairment undergoing exercise-based cardiac rehabilitation program: a randomized controlled trial.

Alzheimer's research & therapy·2026
Same author

A Brief Intervention for the Treatment of Anxiety in Pregnancy: A Pilot Randomized Controlled Trial (The TAP Study).

Psychotherapy and psychosomatics·2026
Same author

ODC1 restricts meningeal B cell age-associated-like phenotype and function in multiple sclerosis: A human and experimental study.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Tissue MicroRNAs in Arrhythmogenic Cardiomyopathy: A Systematic Review of Studies in Human Myocardium and Animal Models with Implications for Post-Mortem Molecular Diagnostics.

Genes·2026
Same journal

Genetic Variants and Dental Caries Susceptibility: An Umbrella Review and Multilevel Meta-Analysis.

Genes·2026
Same journal

Generative AI and Language Models in Human Genetics and Health: From Variant Interpretation to Clinical Decision Support.

Genes·2026
Same journal

Familial White-Sutton Syndrome Caused by a Pathogenic POGZ p.Arg508* Variant: Intrafamilial Variability from Childhood to Adulthood.

Genes·2026
Same journal

Genetic Influence on LDL-Cholesterol Levels: Role of Polygenic Risk Scores and Lp(a) Beyond Monogenic Hypercholesterolemia.

Genes·2026
Same journal

THBS1 as a Key Regulator of Myoblasts: Validation of Its Inhibitory Roles in Skeletal Muscle Development.

Genes·2026
See all related articles

Related Experiment Video

Updated: Jul 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine

Audrey Shiner1,2,3, Alex Kiss4, Khadijeh Saednia1,5

  • 1Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada.

Genes
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models predict the site of distant breast cancer metastasis. Factors like estrogen receptor status and chemotherapy predict bone, brain, or visceral spread, aiding surveillance strategies.

Keywords:
breast cancer metastasismachine learningmetastatic patternsprediction models

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

8.3K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K

Related Experiment Videos

Last Updated: Jul 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
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

8.3K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K

Area of Science:

  • Oncology
  • Biostatistics
  • Machine Learning in Medicine

Background:

  • Distant metastases (DM) occur in up to 30% of breast cancer (BC) patients, with no cure available.
  • Predicting the site of DM is crucial for developing targeted surveillance and treatment strategies.

Purpose of the Study:

  • To develop statistical and machine learning (ML) models for estimating the risk of site-specific DM after local-regional therapy.
  • To identify clinicopathological and treatment factors associated with the site of first DM in invasive breast cancer patients.

Main Methods:

  • Retrospective analysis of 175 invasive breast cancer patients who developed DM.
  • Utilized multivariate statistical analysis and ML-based gradient boosting machines.
  • Outcome variables included the first site of metastasis (brain, bone, visceral) and time to DM.

Main Results:

  • ML models demonstrated predictive accuracy for DM site (AUCs 0.74-0.75).
  • Estrogen receptor (ER)-positive status was linked to increased bone DM risk.
  • ER-negativity predicted brain metastasis; HER2-positivity and non-anthracycline chemotherapy reduced bone DM risk.
  • Non-anthracycline chemotherapy alone predicted visceral metastasis.

Conclusions:

  • Clinicopathological and treatment variables effectively predict the first site of metastasis in breast cancer using ML models.
  • These predictive models show potential for guiding patient-specific surveillance practices for distant metastases.
  • Further validation is warranted to refine these predictive tools for clinical application.