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Updated: Jul 7, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.

Guangming Yi¹, Lihua Deng², Liping Su³

¹Department of Oncology, The Third Hospital of Mianyang (Sichuan Mental Health Center), Mianyang, Sichuan, 621000, People's Republic of China.

Breast Cancer (Dove Medical Press)

|July 6, 2026

Summary

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This summary is machine-generated.

Radiomics and deep learning extract quantitative imaging features from breast cancer scans. This radiogenomics approach links imaging phenotypes to genomic profiles for personalized medicine.

Area of Science:

Oncology
Medical Imaging
Bioinformatics

Background:

Breast cancer is a complex disease with significant heterogeneity.
Radiomics offers a noninvasive method to quantify tumor characteristics from medical images.
Bridging imaging phenotypes with molecular profiles is crucial for personalized medicine.

Purpose of the Study:

To systematically review the radiomics workflow for breast cancer.
To highlight the role of deep learning in radiomics.
To explore the potential of radiogenomics in precision oncology.

Main Methods:

Image acquisition and preprocessing.
Tumor segmentation and radiomic feature extraction.
Feature selection, deep learning models, and multimodal data fusion.

Keywords:

breast cancer deep learning genomic heterogeneity imaging phenotypes precision medicine radiogenomics radiomics

Related Experiment Videos

Last Updated: Jul 7, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Main Results:

Radiomics can extract rich quantitative features reflecting tumor heterogeneity.
Deep learning automates feature extraction and enhances predictive accuracy.
Radiogenomics reveals associations between imaging features and genomic alterations.

Conclusions:

Radiomics and deep learning integration provides novel insights into breast cancer.
This radiogenomics approach supports personalized diagnosis, prognosis, and treatment.
It fosters advancements in precision medicine for breast cancer care.