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

Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.

Cancer research·2026
Same author

Histologic spectrum of BRCA-associated pancreatic ductal adenocarcinoma: A descriptive morphologic study.

Human pathology·2026
Same author

Thromboangiitis Obliterans of the Colon: An Unusual Manifestation of Buerger's Disease.

The Israel Medical Association journal : IMAJ·2026
Same author

Histological activity predicts relapse in pediatric ulcerative colitis despite mucosal healing: a multicenter study from the pediatric IBD Porto group of ESPGHAN.

Journal of Crohn's & colitis·2026
Same author

Living evidence-informed guideline on the early detection of oral squamous cell carcinoma and potentially malignant disorders: Light-based adjuncts to determine the need for biopsy, Version 2026 1.0.

Journal of the American Dental Association (1939)·2026
Same author

Accessible Clinical Tool for Prognosis and Chemotherapy Prediction in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer in Diverse and Low-Resource Settings.

JCO global oncology·2026
Same journal

From Chaos to Care: Personalized AI for Early Cardiac Arrhythmia Warning.

medRxiv : the preprint server for health sciences·2026
Same journal

Large distant deletion disrupts CDKN2A enhancer and predisposes to melanoma.

medRxiv : the preprint server for health sciences·2026
Same journal

Artificial Intelligence-Based Chatbots in Genetic Counseling Practice: Current Uptake, Utilization, and Perspectives.

medRxiv : the preprint server for health sciences·2026
Same journal

Longitudinal MAP-MRI-based Assessment of Tissue Microstructural Alterations in Acute mTBI.

medRxiv : the preprint server for health sciences·2026
Same journal

A class of deep intronic <i>IGHMBP2</i> variants activate a shared cryptic splice donor, enabling correction of select variants with a single antisense oligonucleotide.

medRxiv : the preprint server for health sciences·2026
Same journal

Global Socioeconomic Context and Brain Ageing in Epilepsy: an ENIGMA-Epilepsy study.

medRxiv : the preprint server for health sciences·2026
See all related articles

Related Experiment Video

Updated: Sep 15, 2025

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

Deep Learning on Histopathological Images to Predict Breast Cancer Recurrence Risk and Chemotherapy Benefit.

Gil Shamai1, Shachar Cohen1, Yoav Binenbaum2,3,4

  • 1Taub Faculty of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel.

Medrxiv : the Preprint Server for Health Sciences
|July 15, 2025
PubMed
Summary
This summary is machine-generated.

An artificial intelligence model estimates genomic risk scores from pathology images for early breast cancer, improving chemotherapy decisions where genomic tests are inaccessible. This AI tool aids precision medicine and reduces overtreatment.

Keywords:
Breast CancerDigital PathologyPrecision Oncology

More Related Videos

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

6.9K
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

1.5K

Related Experiment Videos

Last Updated: Sep 15, 2025

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
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

6.9K
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

1.5K

Area of Science:

  • Oncology
  • Artificial Intelligence in Medicine
  • Pathology

Background:

  • Genomic testing is crucial for early breast cancer treatment but faces accessibility issues globally.
  • Hormone receptor-positive, HER2-negative (HR+/HER2-) early breast cancer treatment decisions are often guided by genomic scores.
  • High costs and logistical barriers limit access to genomic testing for many patients worldwide.

Purpose of the Study:

  • To develop an artificial intelligence (AI) model to estimate Oncotype DX 21-gene recurrence scores (RS) using histopathology images and clinicopathologic data.
  • To assess the AI model's accuracy in predicting genomic risk and its prognostic value in HR+/HER2- early breast cancer.
  • To evaluate the AI model's utility in guiding chemotherapy decisions, especially in resource-limited settings.

Main Methods:

  • A multimodal deep learning AI model was developed, pre-trained on a large dataset of histopathological slides.
  • The AI model was fine-tuned and validated using data from the TAILORx randomized trial (n=8,284).
  • External validation was performed on six independent cohorts (n=5,497 patients) to assess generalization.

Main Results:

  • The AI model accurately estimated recurrence scores, achieving an AUC of 0.898 for predicting high genomic risk (RS≥26).
  • AI-driven risk stratification demonstrated significant prognostic value for recurrence-free and disease-free survival.
  • The model identified chemotherapy benefit in premenopausal high-risk patients and ruled it out in postmenopausal low-risk patients, aligning with clinical trial data and reclassifying ~30% of MINDACT high-risk postmenopausal cases as low-risk.

Conclusions:

  • AI applied to standard histopathology images provides a valuable, accessible tool for chemotherapy decision-making in HR+/HER2- early breast cancer.
  • This AI approach can help reduce unnecessary chemotherapy and advance precision medicine, particularly in settings with limited access to genomic testing.
  • The AI model demonstrates robust generalization and prognostic capability, offering a scalable solution for personalized cancer care.