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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluation of CNS xenograft brain tumour response to MRI-guided focused ultrasound in combination with radiation therapy.

British journal of cancer·2026
Same author

Early tumour size changes from neoadjuvant chemotherapy as a predictor of pathologic response in breast cancer.

PloS one·2026
Same author

MRI-guided adaptive radiotherapy for high grade glioma (UNITED): a single-centre, single-arm, non-inferiority, phase 2 trial.

The Lancet. Oncology·2026
Same author

Assessing the benefits of using plane wave compounding when estimating backscatter coefficients with an in situ bead as a calibration target.

The Journal of the Acoustical Society of America·2026
Same author

Machine learning for the prediction of three-year survival in locally advanced breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound imaging.

Scientific reports·2026
Same author

Quantitative Ultrasound Texture Analysis of Breast Tumor Responses to Chemotherapy: Comparison of a Cart-Based and a Wireless Ultrasound Scanner.

Journal of imaging·2026

Related Experiment Video

Updated: Jan 18, 2026

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

8.6K

Hybrid Feature Selection for Predicting Chemotherapy Response in Locally Advanced Breast Cancer Using Clinical and CT

Amir Moslemi1,2, Laurentius Oscar Osapoetra1,2, Aryan Safakish1,3

  • 1Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada.

Cancers
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

Predicting neoadjuvant chemotherapy (NAC) response in locally advanced breast cancer (LABC) is crucial. Combining clinical and CT radiomics features with machine learning accurately predicts NAC treatment effectiveness.

Keywords:
CTLABChybrid feature selectionradiomic

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

7.4K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.5K

Related Experiment Videos

Last Updated: Jan 18, 2026

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

8.6K
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

7.4K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.5K

Area of Science:

  • Oncology
  • Radiology
  • Machine Learning

Background:

  • Neoadjuvant chemotherapy (NAC) is a key treatment for locally advanced breast cancer (LABC).
  • Predicting patient response to NAC before treatment is vital for optimizing therapeutic strategies.
  • Accurate prediction aids in tailoring treatment plans and improving patient outcomes.

Purpose of the Study:

  • To develop a machine learning pipeline for predicting tumor response to NAC in LABC patients.
  • To evaluate the efficacy of combining clinical and radiomics computed tomography (CT) features for treatment response prediction.

Main Methods:

  • A hybrid feature selection method combining filter-based techniques (matrix rank theorem) and a genetic algorithm with Support Vector Machine (SVM) classifier was employed.
  • Dimensionality reduction was performed due to a high feature-to-sample ratio (858 features for 117 patients).
  • Performance was evaluated using balanced accuracy, accuracy, AUC, and F1-score for three models: clinical features only, radiomics CT features only, and a combination of both.

Main Results:

  • The study included 117 LABC patients (mean age 52 ± 11), with 82 responders and 35 non-responders to NAC.
  • The combined model using clinical and CT radiomics features achieved the highest performance with an accuracy of 0.88.
  • This integrated approach demonstrated superior predictive capability compared to using either feature set alone.

Conclusions:

  • The combination of clinical features and CT radiomic features provides an effective strategy for predicting NAC treatment response in LABC patients.
  • This machine learning approach holds promise for personalized medicine in breast cancer treatment.
  • Further validation on larger cohorts is warranted to confirm these findings.