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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...

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Deep Learning-Based Synthetic Contrast-Enhanced Breast MRI for Monitoring Response to Neoadjuvant Therapy.

Suleeporn Sujichantararat1, Debosmita Biswas1,2, Anum S Kazerouni1

  • 1Department of Radiology, University of Washington, Seattle, WA 98195, USA.

Cancers
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Summary
This summary is machine-generated.

This study shows that synthetic contrast-enhanced MRI (CE-MRI) can potentially replace traditional CE-MRI for monitoring breast cancer treatment response. Deep learning models can predict favorable outcomes without gadolinium-based contrast agents (GBCAs).

Keywords:
MRIbreastcancergadoliniumneoadjuvant therapy (NAT)pathologic complete response (pCR)residual cancer burden (RCB)synthetic contrast-enhanced MRI modelingtreatment response

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Contrast-enhanced breast MRI (CE-MRI) is crucial for assessing breast cancer extent and treatment response.
  • Gadolinium-based contrast agents (GBCAs) in CE-MRI pose risks and increase costs.
  • Deep learning (DL) offers a potential solution to reduce GBCA use.

Purpose of the Study:

  • To explore the feasibility of using a DL model to synthesize CE-MRI from non-contrast MRI for breast cancer treatment monitoring.
  • To evaluate the accuracy of synthetic CE-MRI in measuring tumor volume changes and predicting treatment outcomes.

Main Methods:

  • A retrospective pilot study involving women undergoing neoadjuvant therapy (NAT) for breast cancer.
  • A pre-trained DL model synthesized CE-MRI from T1-, T2-, and diffusion-weighted MRI.
  • Tumor volumes and prediction of residual cancer burden (RCB) class 0/1 were compared between synthetic and acquired CE-MRI.

Main Results:

  • Synthetic CE-MRI showed strong correlation with acquired CE-MRI for tumor volumes pre-treatment and early in treatment.
  • Agreement in tumor volume measurement decreased at mid-treatment.
  • Synthetic CE-MRI demonstrated comparable performance to acquired CE-MRI in predicting favorable RCB class (0/1 vs. 2/3) outcomes.

Conclusions:

  • Synthetic CE-MRI shows preliminary feasibility as a GBCA-free alternative for predicting favorable breast cancer treatment outcomes.
  • Further model refinement and validation are needed due to inconsistencies in tumor volume measurements compared to acquired CE-MRI.