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: Apr 8, 2026

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
09:29

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Published on: March 20, 2020

19.2K

An Exploratory Study on Prognostic Prediction and Interpretability Analysis in Early-stage Triple-negative Breast

Zi Xuan Yang1, Ya Ping Lyu2, Liu Liu Quan1

  • 1National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

Biomedical and Environmental Sciences : BES
|April 7, 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

Stress-Shield Enhanced Fatigue Resistance in Fabric-Hydrogel Composite Coatings.

ACS applied materials & interfaces·2026
Same author

MCLAM: a cost-effective deep learning model for predicting recurrence risk in HR+/HER2- breast cancer-a multi-center study in a Chinese cohort.

Journal of the National Cancer Center·2026
Same author

Geometry-Engineered Microgrooves Broaden the Material Scope for Spontaneous Liquid Spreading.

Small methods·2026
Same author

Flame Spray Pyrolysis Engineering of Highly Spherical LiMn<sub>0.5</sub>Fe<sub>0.5</sub>PO<sub>4</sub> Nanoparticles With Boosted Volumetric Energy Density for Lithium-Ion Batteries.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Millisecond Engineering of Asymmetric Pt-Ov-Ce Interfaces via Hydrogen-Quenched Flame Spray Pyrolysis for Anti-Poisoning CO Oxidation.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Quantum Dots for Biomedical Biosensing, NIR-II Bioimaging, and Phototherapy: Materials Design, Signal Transduction, and Translational Barriers.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
This summary is machine-generated.

This study developed a deep learning model for early-stage triple-negative breast cancer (TNBC) prognosis using H&E images. The model accurately predicts outcomes and reveals links between image features, immune microenvironment, and gene expression.

Area of Science:

  • Oncology
  • Pathology
  • Computational Biology

Background:

  • Triple-negative breast cancer (TNBC) is an aggressive subtype with limited targeted therapies.
  • Accurate prognostic prediction is crucial for guiding treatment decisions in early-stage TNBC.

Purpose of the Study:

  • To develop a prognostic prediction model for early-stage TNBC utilizing H&E-stained pathological images.
  • To investigate the biological interpretability of the developed model by correlating image features with clinical and molecular data.

Main Methods:

  • A deep learning model was trained on 340 whole slide images (WSIs) and validated on 81 TCGA cases.
  • Image-derived features from convolutional neural networks were integrated with clinicopathological variables.
  • Interpretability was assessed by correlating image features with mRNA-seq data and immune microenvironment characteristics.
Keywords:
Deep learningH&E-stained pathological imagesModel interpretabilityPrognostic prediction modelTriple-negative breast cancer

More Related Videos

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment &#8212; Challenges and Innovations in Cancer Prognosis
07:32

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Published on: April 12, 2024

2.2K

Related Experiment Videos

Last Updated: Apr 8, 2026

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
09:29

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Published on: March 20, 2020

19.2K
Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment &#8212; Challenges and Innovations in Cancer Prognosis
07:32

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Published on: April 12, 2024

2.2K

Main Results:

  • The model achieved AUCs of 0.86 (training) and 0.75 (validation).
  • Lymphocyte abundance, identified via HoVer-Net, was associated with recurrence risk.
  • Texture-related image features correlated significantly with immune cell infiltration and prognostic gene expression.

Conclusions:

  • Deep learning models can accurately predict prognosis in early-stage TNBC from H&E images.
  • Interpretable image features reflect the tumor immune microenvironment and gene expression profiles, offering biological insights.