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

Cancer Survival Analysis01:21

Cancer Survival Analysis

456
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
456

You might also read

Related Articles

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

Sort by
Same author

Impact of semaglutide pretreatment on reproductive outcomes in women with overweight and obesity with infertility: a real-world multicenter cohort study.

Reproductive biology and endocrinology : RB&E·2026
Same author

Using Virtual Reality to Assess Spatial Navigation Ability in Individuals With Mild Cognitive Impairment and Older Adults: Cross-Sectional Study.

JMIR aging·2025
Same author

Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach.

Journal of medical Internet research·2024
Same author

A Study of Disease Prognosis in Lung Adenocarcinoma Using Single-Cell Decomposition and Immune Signature Analysis.

Cancers·2024
Same author

COL6A3 Exosomes Promote Tumor Dissemination and Metastasis in Epithelial Ovarian Cancer.

International journal of molecular sciences·2024
Same author

Left Ventricular Geometry and Inferior Vena Cava Diameter Co-Modify the Risk of Cardiovascular Outcomes in Chronic Hemodialysis Patients.

Medicina (Kaunas, Lithuania)·2024

Related Experiment Video

Updated: Sep 14, 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

Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development

Chun-Chi Lai1,2, Cheng-Yu Chen3,4,5,6,7, Tzu-Hao Chang1,8

  • 1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 9th Floor, 301 Yuantong Road, Zhonghe District, Taipei, Taiwan, 886 66202589 ext 10928.

JMIR Cancer
|July 18, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict pathological complete response (pCR) in breast cancer patients receiving neoadjuvant therapy. Incorporating breast sonography data showed a modest improvement in predicting pCR, suggesting its potential value in clinical workflows.

Keywords:
breast cancerbreast sonographymachine learningneoadjuvant therapypathological complete response

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

199
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

Related Experiment Videos

Last Updated: Sep 14, 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
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

199
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

Area of Science:

  • Oncology
  • Medical Informatics
  • Machine Learning

Background:

  • Breast cancer is a leading global cancer, with neoadjuvant therapy showing improved outcomes.
  • Pathological complete response (pCR) is a key prognostic marker in breast cancer treatment.
  • Predicting pCR is crucial for tailoring neoadjuvant therapy and improving patient survival.

Purpose of the Study:

  • To develop machine learning models for predicting pCR after neoadjuvant therapy for breast cancer.
  • To evaluate the impact of clinical, laboratory, and imaging data on prediction accuracy.
  • To identify optimal features and algorithms for robust pCR prediction.

Main Methods:

  • A retrospective cohort study analyzed 334 breast cancer patients (2015-2022).
  • Machine learning models (logistic regression, random forest, SVM, XGBoost) were trained using clinical, laboratory, and sonography data.
  • Recursive feature elimination with cross-validation was used for feature selection.

Main Results:

  • Logistic regression with recursive feature elimination demonstrated optimal performance.
  • Models incorporating clinical data achieved an AUROC of 0.73.
  • Adding laboratory data did not significantly improve prediction; breast sonography showed a modest enhancement in a subset of patients.

Conclusions:

  • Machine learning models can predict pCR in breast cancer patients undergoing neoadjuvant therapy.
  • Breast sonography data may offer additional value for pCR prediction, warranting further investigation.
  • Validated models integrating sonography could enhance clinical decision-making for neoadjuvant breast cancer treatment.