Jove
Visualize
Contact Us

Related Concept Videos

Cardiomyopathy I: Introduction and Classification01:25

Cardiomyopathy I: Introduction and Classification

Cardiomyopathy, or CMP, is a group of diseases affecting the myocardial structure, impairing its ability to pump blood effectively. This condition can lead to arrhythmias, heart failure, or sudden cardiac death.Cardiomyopathies are classified into primary and secondary categories:Primary Cardiomyopathy refers to conditions involving only the heart muscle that are often idiopathic (of unknown cause) or genetic. They primarily affect the myocardium without the involvement of other systemic...
Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K
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
  1. Home
  3. Unsupervised Machine Learning Identifies Distinct Phenotypes In Cardiac Complications Of Pediatric Patients Treated With Anthracyclines.
  1. Home
  3. Unsupervised Machine Learning Identifies Distinct Phenotypes In Cardiac Complications Of Pediatric Patients Treated With Anthracyclines.

Related Experiment Video

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.4K

Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated

Xander Jacquemyn1,2, Bhargava K Chinni1, Benjamin T Barnes1

  • 1Department of Pediatrics, The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Johns Hopkins School of Medicine, Johns Hopkins University, Johns Hopkins Hospital, 600 N. Wolfe Street, 1389 Blalock, Baltimore, MD, 21287, USA.

Cardio-Oncology (London, England)
|October 29, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning identified distinct patient groups in pediatric cancer treatment, revealing varied risks for heart dysfunction and high blood pressure during anthracycline chemotherapy for personalized care.

Keywords:
AnthracyclineCancer therapy–related cardiac dysfunctionCardiotoxicityEchocardiographyMachine learning

More Related Videos

Hybrid Cell Analysis System to Assess Structural and Contractile Changes of Human iPSC-Derived Cardiomyocytes for Preclinical Cardiac Risk Evaluation
08:03

Hybrid Cell Analysis System to Assess Structural and Contractile Changes of Human iPSC-Derived Cardiomyocytes for Preclinical Cardiac Risk Evaluation

Published on: October 20, 2022

1.7K
Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish
09:15

Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

Published on: February 6, 2020

5.0K

Related Experiment Videos

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.4K
Hybrid Cell Analysis System to Assess Structural and Contractile Changes of Human iPSC-Derived Cardiomyocytes for Preclinical Cardiac Risk Evaluation
08:03

Hybrid Cell Analysis System to Assess Structural and Contractile Changes of Human iPSC-Derived Cardiomyocytes for Preclinical Cardiac Risk Evaluation

Published on: October 20, 2022

1.7K
Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish
09:15

Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

Published on: February 6, 2020

5.0K

Area of Science:

  • Cardio-oncology
  • Pediatric oncology
  • Machine learning in medicine

Background:

  • Anthracyclines are vital for pediatric cancer treatment but pose risks of cancer therapy-related cardiac dysfunction (CTRCD).
  • Standardized definitions by the International Cardio-Oncology Society (IC-OS) aim to improve CTRCD risk assessment precision.

Purpose of the Study:

  • To categorize distinct phenotypes in pediatric patients receiving anthracycline chemotherapy using unsupervised machine learning.
  • To identify patient subgroups with varying risks of CTRCD and hypertensive response.

Main Methods:

  • Retrospective analysis of pediatric cancer patients undergoing anthracycline chemotherapy.
  • Utilized unsupervised machine learning (principal component analysis, K-means clustering) to identify phenotypic clusters.
  • Analyzed phenogroups for associations with CTRCD and hypertensive response based on IC-OS definitions.

    • Main Results:

    • Four distinct phenogroups were identified among 187 pediatric patients.
    • Cluster 0 exhibited a significantly higher risk of moderate CTRCD (HR: 3.10).
    • Cluster 3 showed a protective effect against hypertensive response (HR: 0.30).
    • Longitudinal data revealed differences in global longitudinal strain and blood pressure among phenogroups.

    Conclusions:

    • Unsupervised machine learning successfully identified distinct phenogroups in pediatric cancer patients treated with anthracyclines.
    • These phenogroups offer potential for personalized risk assessment and management of cardiotoxicity.