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

Malignant adenomyoepithelioma of the breast: seven cases illustrating morphological diversity and diagnostic challenges.

Histopathology·2026
Same author

Modeling Zn<sub>3</sub>O<sub>3</sub>/Ga<sub>3</sub>O<sub>3</sub> composite for water splitting: a first-principle DFT study of electronic structure and interfacial reactivity.

Scientific reports·2026
Same author

Oncostatin level and Tumour necrosis factor-alpha gene polymorphism in predicting Blastocystis infection in inflammatory bowel disease.

Acta tropica·2026
Same author

Changes in CD8-positive lymphocytes following chemotherapy with concomitant bevacizumab in HER2-negative breast cancer.

Breast cancer (Tokyo, Japan)·2026
Same author

DFT study on tunable electronic and adsorption properties of poly(vinyl alcohol)/copper oxide/graphene oxide hybrid nanostructures.

Scientific reports·2026
Same author

Spontaneous Haemothorax as Initial Presentation of Pleural Ewing Sarcoma: A Case Report.

Case reports in pulmonology·2026

Related Experiment Video

Updated: Jul 6, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.1K

Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application.

Asmaa Ibrahim1, Mostafa Jahanifar2, Noorul Wahab2

  • 1Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Department of Pathology, Faculty of Medicine, Suez Canal University, Egypt.

Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc
|December 28, 2023
PubMed
Summary

Artificial intelligence (AI) shows promise for breast cancer (BC) mitosis scoring. The mitotic activity index (MAI) method using AI is the most reliable, correlating with visual scoring and predicting patient survival.

Keywords:
algorithmartificial intelligencemitosis

More Related Videos

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.3K
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

7.2K

Related Experiment Videos

Last Updated: Jul 6, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.1K
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
08:32

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

6.3K
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

7.2K

Area of Science:

  • Computational pathology
  • Breast cancer diagnostics
  • Artificial intelligence in oncology

Background:

  • Artificial intelligence (AI) excels at mitosis identification and quantification in cancer.
  • Clinical implementation of AI for breast cancer (BC) requires evaluation against established methods.
  • Accurate mitotic figure scoring is crucial for BC grading and prognosis.

Purpose of the Study:

  • To assess the optimal method for AI-based mitotic figure scoring in breast cancer (BC).
  • To compare AI-derived mitotic counts (mitotic count per tumor area [MCT], mitotic index [MI], mitotic activity index [MAI]) with visual scoring and clinical outcomes.

Main Methods:

  • Utilized whole slide images from large BC cohorts (Nottingham, TCGA-BRCA).
  • Employed automated mitosis detection to calculate MCT, MI, and MAI.
  • Evaluated AI metrics against the Nottingham grading system's visual scoring, Ki67 scores, clinicopathologic parameters, and patient survival.

Main Results:

  • All AI-based mitotic scores (MCT, MI, MAI) correlated with clinicopathologic characteristics and survival (P < .001).
  • Only MAI and MCT showed positive correlation with the gold standard visual scoring (r=0.8, r=0.7) and Ki67 scores (r=0.69, r=0.55).
  • MAI was the sole independent predictor of survival in multivariate analysis (P < .05).

Conclusions:

  • The optimal AI method for BC mitosis scoring needs careful consideration for clinical application.
  • Mitotic activity index (MAI) using AI provides reliable, reproducible, and accurate quantification of mitotic figures in breast cancer.
  • MAI demonstrates superior performance as an independent prognostic marker in breast cancer.