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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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Related Experiment Video

Updated: Jun 21, 2026

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound
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Longitudinal MRI-Driven Multi-Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration

Yu-Hong Huang1, Zhen-Yi Shi2,3, Teng Zhu1

  • 1Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Second Road, Yuexiu District, Guangzhou, Guangdong Province, 510080, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|February 8, 2025
PubMed
Summary

Predicting breast cancer treatment response is difficult. A new model using MRI, transcriptomics, and single-cell sequencing accurately predicts pathological complete response (pCR) to neoadjuvant treatment (NAT).

Keywords:
artificial intelligencebreast cancermedical imagingmulti‐omics analysisneoadjuvant treatment

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Area of Science:

  • Oncology
  • Radiology
  • Genomics

Background:

  • Predicting pathological complete response (pCR) to neoadjuvant treatment (NAT) in breast cancer is challenging due to tumor heterogeneity.
  • Accurate prediction of pCR is crucial for tailoring treatment and improving patient outcomes.

Purpose of the Study:

  • To develop and validate a novel multi-modality model integrating longitudinal MRI spatial habitat radiomics, transcriptomics, and single-cell RNA sequencing for predicting pCR in breast cancer patients undergoing NAT.
  • To assess the model's performance and robustness across diverse patient subgroups and compare it with traditional radiomics approaches.

Main Methods:

  • Enrolled 2279 patients across 12 centers.
  • Developed a multi-modality model integrating longitudinal MRI spatial habitat radiomics, transcriptomics, and single-cell RNA sequencing.
  • Analyzed tumor subregions on multi-timepoint MRI to capture dynamic intra-tumoral heterogeneity during NAT.

Main Results:

  • The multi-modality model demonstrated superior performance in predicting pCR, achieving areas under the curve (AUC) of 0.863 (external validation), 0.813 (immunotherapy cohort), and 0.888 (multi-omics cohort).
  • The model showed robustness across varying molecular subtypes and clinical stages.
  • High model scores correlated with increased immune activity, specifically elevated B cell infiltration, suggesting a biological basis for the imaging-derived predictions.

Conclusions:

  • The integration of spatial habitat radiomics with molecular data provides a powerful, noninvasive tool for monitoring dynamic changes in tumor heterogeneity during NAT.
  • This novel model shows significant promise for accurately predicting pCR, potentially guiding treatment decisions and improving breast-conserving surgery rates.
  • Prospective validation is recommended to confirm the model's clinical utility across diverse patient populations and settings.