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: May 10, 2026

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

A quantifier-based fuzzy classification system for breast cancer patients.

Daniele Soria1, Jonathan M Garibaldi, Andrew R Green

  • 1School of Computer Science, Advanced Data Analysis Centre, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK. daniele.soria@nottingham.ac.uk

Artificial Intelligence in Medicine
|June 25, 2013
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

Added value of the tumor-stroma ratio to the PREDICT model for breast cancer.

Breast (Edinburgh, Scotland)·2026
Same author

DHX37 protein and mRNA expression patterns in breast and ovarian cancer and their prognostic implications.

Histochemistry and cell biology·2026
Same author

Targeting Glutaminase Isoforms GLS and GLS2 in Luminal Breast Cancer.

International journal of molecular sciences·2026
Same author

Oscillatory Correlates of Habituation: EEG Evidence of Sustained Frontal Theta Activity to Food Cues.

Sensors (Basel, Switzerland)·2026
Same author

The use of fully immersive virtual reality for screening neurodegenerative diseases: A systematic review of behavioral and diagnostic outcomes.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

Functional and clinical significance of the RNA m<sup>6</sup>A methyltransferase complex in breast cancer.

NPJ breast cancer·2025

This study introduces a fuzzy algorithm to classify breast cancer patients into seven clinical groups. The new method creates easy-to-understand rules, classifying over 95% of patients for better clinical usability.

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Oncology

Background:

  • Seven distinct clinical phenotypes of breast cancer have been identified using immunohistochemistry.
  • Existing unsupervised classification methods achieve consensus but often leave patients unclassified.
  • Fuzzy methodologies offer potential for linguistic-based classification rules.

Purpose of the Study:

  • To investigate fuzzy methodologies for creating interpretable classification rules for breast cancer.
  • To develop a system capable of classifying the majority of patients into predefined clinical groups.
  • To refine previously identified breast cancer groups using a data-driven fuzzy rule-based system.

Main Methods:

  • Extended a fuzzy quantification subsethood-based algorithm with a novel class assignment procedure.
Keywords:
Breast cancerFuzzy rulesLinguistic rulesetRule-based classification

More Related Videos

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model
08:08

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model

Published on: June 11, 2014

Related Experiment Videos

Last Updated: May 10, 2026

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

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model
08:08

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model

Published on: June 11, 2014

  • Applied the system to a breast cancer dataset of over 1000 patients with ten protein markers.
  • Utilized statistical approaches to compare results with previous groupings and assess unclassified patients.

    • Main Results:

    • Generated a classification rule set with labels (High, Low, Omit) for each biomarker.
    • Achieved high agreement (Kendall's Tau = 0.9) with original reference class distribution.
    • Classified 1035 out of 1073 patients, leaving only 38 unclassified.

    Conclusions:

    • The fuzzy algorithm provides a simple, linguistic rule set for breast cancer classification.
    • Successfully classifies over 95% of patients into one of seven clinical groups.
    • Represents a more clinically usable class assignment algorithm.