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 21, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Mid-Treatment Delta MRI Radiomics Enhances Sensitivity and Risk Stratification for Pathological Complete Response in

Hong Li1, Ruofan Hu2, Weiqing Huang1

  • 1Department of Radiology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510260, China (H.L., W.H., S.H., H.C.).

Academic Radiology
|May 19, 2026
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

Stiffness-Switchable Conductive Nanocomposites with Temperature-Invariant Conductivity for Long-Term Brain-Computer Interfaces on Hair-Covered Scalp.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using a Three-tier MRI Radiomics Model with Nested Cross-validation.

Academic radiology·2026
Same author

Faradaic Reversible Electrodes Enable Programmable High-Power Hydrovoltaic Energy Harvesting Across Broad Ionic Environments.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Deep learning-based bone removal in head and neck computed tomography angiography: a comparative study with conventional algorithms.

Quantitative imaging in medicine and surgery·2025
Same author

Deep learning-empowered triboelectric acoustic textile for voice perception and intuitive generative AI-voice access on clothing.

Science advances·2025
Same author

Patient-tailored versus fixed post-trigger delay for optimized scan timing: a comparison of image quality and diagnostic confidence in run-off CT angiography.

European radiology·2025
Same journal

Deep Learning for Opportunistic Vertebral Fracture Detection on Routine Thoraco-abdominal Computed Tomography: A Systematic Review and Hierarchical Summary Receiver Operating Characteristic Meta-analysis of Patient-level Diagnostic Test Accuracy.

Academic radiology·2026
Same journal

"Where are They Now?": A Single Institution's 10-Years Experience with an Integrated Nuclear Radiology Fellowship.

Academic radiology·2026
Same journal

Dual-layer Spectral Detector CT Quantitative Parameters for Predicting Tumor Budding Grade and Prognosis in Stage Ⅱ Colorectal Cancer.

Academic radiology·2026
Same journal

Promotion from Associate Professor to Full Professor Should Not Be Monolithic: A National Bibliometric Study by Radiology Subspecialty.

Academic radiology·2026
Same journal

Technological Lag of Digitization for Patient Image Transfer.

Academic radiology·2026
Same journal

Prognostic Value of Coronary Sinus Flow and Aortic Pressure Gradient Quantified by 4D Flow CMR in AMI.

Academic radiology·2026
See all related articles
This summary is machine-generated.

Predicting pathological complete response (pCR) in breast cancer is challenging. Mid-treatment MRI delta radiomics combined with clinical data offers high sensitivity for pCR prediction, aiding treatment decisions.

Area of Science:

  • Radiology and Medical Imaging
  • Oncology
  • Machine Learning in Medicine

Background:

  • Accurate prediction of pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer is crucial for treatment optimization.
  • Current methods for predicting pCR often face limitations in accuracy and early prediction capabilities.

Purpose of the Study:

  • To develop and validate mid-treatment magnetic resonance imaging (MRI) delta radiomics models for predicting pCR in breast cancer patients.
  • To assess the performance of combined clinical and delta radiomics models compared to clinical features alone.

Main Methods:

  • A multicenter retrospective study involving 242 breast cancer patients.
  • Extraction of delta radiomics features from mid-treatment vs. pre-treatment T1-weighted contrast-enhanced MRI sequences.
Keywords:
Breast cancerDelta RadiomicsNeoadjuvant chemotherapyPathological complete responseSHapley Additive exPlanations(SHAP)

More Related Videos

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models
09:18

Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models

Published on: February 3, 2026

Related Experiment Videos

Last Updated: May 21, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models
09:18

Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models

Published on: February 3, 2026

  • Development of six models using Random Forest, combining clinical and delta radiomics features, validated internally and externally.

    • Main Results:

    • The combined clinical-delta radiomics model achieved an external validation AUC of 0.747, showing comparable performance to the clinical-only model (AUC=0.777).
    • The combined model significantly improved sensitivity (90.6%) compared to the clinical-only model (65.6%) for pCR prediction.
    • Effective risk stratification was demonstrated, with a 51%-point separation in pCR rates between predicted responder and non-responder groups.

    Conclusions:

    • Mid-treatment delta MRI radiomics, when combined with clinical features, provides high sensitivity and effective risk stratification for pCR prediction in breast cancer.
    • While not significantly improving AUC over clinical features alone, the enhanced sensitivity and risk stratification suggest potential clinical utility.
    • Larger prospective validation studies are warranted to confirm the clinical applicability of these findings.