Advancing Radiogenomics: MRI-Guided Identification of Novel Breast Cancer Molecular Subtypes

Motivation: Current radiogenomics studies are limited in linking image phenotypes with genomic phenotypes, and their clinical translation value remains inadequately explored. Goal(s): Define novel breast cancer subtype using only image-associated genes and evaluate its prognostic and therapeutic importance. Approach: Extract a comprehensive image profile, identify image-associated genes, and define an image-inspired genomic subtype (I2G-C). Results: The new subtype, I2G-C, is distinct from traditional subtypes, characterized by unique immune and repair functions. I2G-C shows significant predictive value for patients receiving anti-HER2 therapies and identifies high-risk patients in terms of disease-free survival. Impact: This study advances radiogenomics by demonstrating functional MRI-guided identification of new breast cancer subtypes, highlighting its potential in clinical applications.

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/10.58530/2025/2128
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Advancing Radiogenomics: MRI-Guided Identification of Novel Breast Cancer Molecular Subtypes

Tian Li, Xiaodong Teng, Qingpei Lai, Jing Cai 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
Radiomics and Machine Learning in Medical Imaging
article

Advancing Radiogenomics: MRI-Guided Identification of Novel Breast Cancer Molecular Subtypes

Tian Li, Xiaodong Teng, Qingpei Lai, Jing Cai, Xinyu Zhang, Jiang Zhang, Guosheng Ren
article en

Abstract

Motivation: Current radiogenomics studies are limited in linking image phenotypes with genomic phenotypes, and their clinical translation value remains inadequately explored. Goal(s): Define novel breast cancer subtype using only image-associated genes and evaluate its prognostic and therapeutic importance. Approach: Extract a comprehensive image profile, identify image-associated genes, and define an image-inspired genomic subtype (I2G-C). Results: The new subtype, I2G-C, is distinct from traditional subtypes, characterized by unique immune and repair functions. I2G-C shows significant predictive value for patients receiving anti-HER2 therapies and identifies high-risk patients in terms of disease-free survival. Impact: This study advances radiogenomics by demonstrating functional MRI-guided identification of new breast cancer subtypes, highlighting its potential in clinical applications.

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 66%
Radiomics and Machine Learning in Medical Imaging
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