Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 31, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related

Christopher Yu¹, Mohammad Peikari², Dina Labib³

¹Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.

JACC. Cardiovascular Imaging

|May 29, 2026

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

The Burden of Cardiovascular Disease in Lymphoma Survivors: A Population-Based Matched Cohort Study.

JACC. Advances·2026

Same author

Elevations in Donor-Derived Cell-Free DNA and Allograft Outcomes in Kidney Transplantation.

Journal of the American Society of Nephrology : JASN·2026

Same author

REPLY: Beyond Global Viability: Patient-Specific Identification of Jeopardized Myocardium to Guide Revascularization.

JACC. Cardiovascular imaging·2026

Same author

Comparison of donor-derived cell-free DNA fractions between combined heart-lung vs isolated heart or lung transplant recipients.

JHLT open·2026

Same author

Evaluating the Temporal and Sociodemographic Generalizability of the Emergency Heart Failure Mortality Risk Grade.

ESC heart failure·2026

Same author

Brain viral reservoir seeding and neurological metabolic dysregulation in early-life immunodeficiency virus infection.

Brain pathology (Zurich, Switzerland)·2026

Same journal

Seeing the Unseen: Deep Learning and the Pre-Therapy Cardiac Phenotype in Cardiotoxicity.

JACC. Cardiovascular imaging·2026

Same journal

Measure Simply, Stratify Better: Suspected Myocarditis-The Clinical Case for Atrial LAS in Routine CMR.

JACC. Cardiovascular imaging·2026

Same journal

Expert Human Readers vs ECG Criteria for Detecting Left Ventricular Hypertrophy: A CMR-Based Comparison.

JACC. Cardiovascular imaging·2026

Same journal

Secondary Tricuspid Regurgitation in Pulmonary Hypertension: Marker, Mediator, and Potential Target?

JACC. Cardiovascular imaging·2026

Same journal

Integrated Coronary CT Angiography Assessment of Plaque Vulnerability and Clinical Outcomes: The Morphology-Inflammation-Burden (MIB) Score.

JACC. Cardiovascular imaging·2026

Same journal

Coronary CT Angiography: New Insights in Search of Vulnerable Plaques and Patients.

JACC. Cardiovascular imaging·2026

See all related articles

Summary

This summary is machine-generated.

Deep learning models using cardiac MRI before cancer therapy can predict cardiac dysfunction risk. These AI models outperform traditional clinical and imaging methods for early detection.

Area of Science:

Cardiology
Oncology
Artificial Intelligence
Medical Imaging

Background:

Predicting cancer therapy-related cardiac dysfunction (CTRCD) is a significant clinical challenge.
Early identification of patients at risk is crucial for timely intervention and management.

Purpose of the Study:

To evaluate deep learning (DL) models using pre-treatment cardiac magnetic resonance (CMR) images for predicting CTRCD.
To compare the predictive performance of DL models against conventional clinical and imaging-based risk scores.

Main Methods:

Utilized CMR short-axis cine images from HER2+ breast cancer patients undergoing anthracycline and trastuzumab therapy.
Developed deep convolutional neural network architectures for image-based DL models to predict CTRCD.

Keywords:

cardio-oncology cardiotoxicity cancer therapy–related cardiac dysfunction heart failure machine learning

More Related Videos

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

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

Published on: August 16, 2020

Related Experiment Videos

Last Updated: May 31, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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

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

Published on: August 16, 2020

Validated models internally and externally using data from prospective studies.

Main Results:

The DL model achieved an AUC of 0.85 and F1 score of 0.69 in internal validation for CTRCD prediction.
External validation demonstrated an AUC of 0.80 and F1 score of 0.55 for the DL model.
DL models significantly outperformed clinical risk scores (AUC 0.66) and conventional CMR/echocardiography parameters (AUCs 0.59-0.62).

Conclusions:

DL models leveraging pre-therapy CMR images offer superior prediction of future CTRCD risk.
This AI-driven approach surpasses traditional methods in identifying patients susceptible to cardiotoxicity.
Pre-treatment CMR analysis with DL holds promise for personalized cancer treatment strategies.