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

A Rare Case of Extramammary Paget Disease With Concurrent Herpes Simplex Virus Infection: Three-Color Immunohistochemical Analysis for Re-Evaluating Previous Hypotheses.

Pathology international·2026
Same author

GH responsiveness to corticotropin-releasing hormone identifies corticotroph-like somatotroph adenomas in acromegaly.

Pituitary·2026
Same author

A Homology-Based Application to Diagnose Colorectal Adenoma and Early-Stage Cancer.

Pathology international·2026
Same author

Adrenal Primary Ganglioneuroblastoma Presenting in Adulthood: A Case Report.

IJU case reports·2026
Same author

Pulmonary <i>Mycobacterium shigaense</i> Infection with Hemoptysis: Two Surgically Resected Case Reports.

Surgical case reports·2026
Same author

Fibroblast activation protein inhibitor-PET/CT in lung cancer: a prospective single-center study with histopathological correlation of fibroblast activation protein expression.

International journal of clinical oncology·2026

Related Experiment Video

Updated: Sep 14, 2025

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

9.1K

Integrative Radiogenomics Using MRI Radiomics and Microarray Gene Expression Analysis to Predict Pathological

Soya Oda1, Yukiko Tokuda2, Yuki Suzuki1

  • 1Department of Artificial Intelligence in Diagnostic Radiology, The University of Osaka Graduate School of Medicine, Suita, JPN.

Cureus
|July 21, 2025
PubMed
Summary

Radiogenomics, combining MRI and DNA data, showed potential for predicting neoadjuvant chemotherapy (NAC) response in breast cancer. While not statistically significant, this approach may aid treatment decisions.

Keywords:
breast neoplasmsmagnetic resonance imagingneoadjuvant chemotherapyradiogenomicsradiomics

More Related Videos

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning
10:25

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning

Published on: April 12, 2024

1.6K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K

Related Experiment Videos

Last Updated: Sep 14, 2025

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

9.1K
Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning
10:25

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning

Published on: April 12, 2024

1.6K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K

Area of Science:

  • Oncology
  • Radiology
  • Genomics
  • Machine Learning

Background:

  • Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer is challenging due to variable response rates.
  • Identifying reliable predictive markers is crucial for optimizing treatment strategies and improving patient outcomes.

Purpose of the Study:

  • To evaluate and compare the predictive accuracy of three machine learning models for pCR after NAC in breast cancer patients.
  • Models assessed included radiomics (MRI features), genomics (DNA microarray data), and radiogenomics (integrated MRI and microarray data).
  • The study aimed to determine the most precise non-invasive prediction model using a consistent dataset and analytical pipeline.

Main Methods:

  • A retrospective analysis of 112 breast cancer patients who underwent DNA microarray and MRI before NAC.
  • Patients were categorized into pCR (N=21) and non-pCR (N=91) groups.
  • Model performance was evaluated using repeated stratified nested cross-validation and assessed by ROC-AUC, with statistical significance tested by DeLong's test.

Main Results:

  • The radiogenomics model achieved an AUC of 0.607, outperforming radiomics (AUC 0.563) and genomics (AUC 0.559).
  • Despite the higher AUC, the improvement in prediction accuracy by the radiogenomics model was not statistically significant (p>0.05).

Conclusions:

  • Machine learning-based radiogenomics integrating MRI and DNA microarray data demonstrated improved, though not statistically significant, accuracy in predicting pCR after NAC.
  • These findings highlight the potential of radiogenomics as a non-invasive tool to support clinical decision-making for breast cancer patients receiving NAC.