MRE-derived Tumor Stiffness as a Non-invasive Technique to Characterize Breast Tumors

Motivation: Breast tumors can be detected and characterized using biopsy and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI); however, MR Elastography (MRE) offers a novel, non-invasive diagnostic approach with comparable sensitivity and specificity. Goal(s): Validate the use of MRE to characterize breast tumors by comparing it with typical diagnostic techniques like histological grading and DCE-MRI. Approach: MRE was performed on patients with known breast tumors to measure stiffness and characterize breast tumors, then statistical analysis was conducted to validate the characterization against histological grading and DCE-MRI. Results: MRE-derived tumor stiffness was statistically significantly consistent with clinical grading and DCE-MRI characterization of tumors. Impact: The statistically significant consistency between the non-invasive MRE-derived tumor stiffness and the current gold-standards of diagnosis, histological grading and DCE-MRI, means that breast tumor characterization through MRE is possible while maintaining sensitivity and specificity.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1990
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MRE-derived Tumor Stiffness as a Non-invasive Technique to Characterize Breast Tumors

Kristin Thompson, Jeffrey Hawley, Arunark Kolipaka, Brandy Griffith et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced MRI Techniques and Applications
article

MRE-derived Tumor Stiffness as a Non-invasive Technique to Characterize Breast Tumors

Kristin Thompson, Jeffrey Hawley, Arunark Kolipaka, Brandy Griffith, Akhil Deavela
article en

Abstract

Motivation: Breast tumors can be detected and characterized using biopsy and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI); however, MR Elastography (MRE) offers a novel, non-invasive diagnostic approach with comparable sensitivity and specificity. Goal(s): Validate the use of MRE to characterize breast tumors by comparing it with typical diagnostic techniques like histological grading and DCE-MRI. Approach: MRE was performed on patients with known breast tumors to measure stiffness and characterize breast tumors, then statistical analysis was conducted to validate the characterization against histological grading and DCE-MRI. Results: MRE-derived tumor stiffness was statistically significantly consistent with clinical grading and DCE-MRI characterization of tumors. Impact: The statistically significant consistency between the non-invasive MRE-derived tumor stiffness and the current gold-standards of diagnosis, histological grading and DCE-MRI, means that breast tumor characterization through MRE is possible while maintaining sensitivity and specificity.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 65%
Advanced MRI Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.