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Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic

Benjamin C Musall1, David E Rauch1, Rania M M Mohamed2

  • 1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Journal of Magnetic Resonance Imaging : JMRI
|January 31, 2024
PubMed
Summary
This summary is machine-generated.

Diffusion tensor imaging (DTI) can predict treatment response in triple-negative breast cancer (TNBC) patients undergoing neoadjuvant systemic treatment (NAST). Changes in DTI parameters of tumors and surrounding tissue show significant differences between responders and non-responders.

Keywords:
breast MRIdiffusion tensor imagingperitumoral regiontreatment responsetriple‐negative breast cancer

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Assessing treatment response in triple-negative breast cancer (TNBC) is crucial for personalized patient care.
  • Diffusion tensor imaging (DTI) offers a method to measure tissue changes in tumors and surrounding fibroglandular tissue (FGT) during neoadjuvant systemic treatment (NAST).

Purpose of the Study:

  • To investigate the utility of DTI parameters in predicting treatment response for TNBC patients undergoing NAST.
  • To evaluate if DTI can differentiate between patients who achieve a pathologic complete response (pCR) and those who do not.

Main Methods:

  • A prospective study involving 86 women with clinical stage I-III TNBC receiving NAST.
  • Longitudinal DTI scans (pre-treatment, after 2 and 4 cycles of NAST) were analyzed using histogram features from tumors, peritumoral regions (PTRs), and FGT.
  • Statistical analysis, including Mann-Whitney U test and ROC curve analysis, was used to assess DTI parameter discrimination of pCR.

Main Results:

  • 47% of patients achieved a pCR.
  • DTI parameters in tumors, PTRs, and FGT showed significant differences between pCR and non-pCR groups after 2 and 4 NAST cycles.
  • Changes in PTR anisotropy over time effectively predicted pCR (AUC: 0.78), increasing in responders and decreasing in non-responders.

Conclusions:

  • Quantitative DTI features derived from TNBC tumors and peritumoral tissues show promise for predicting NAST response.
  • DTI analysis may aid in early identification of treatment efficacy, guiding individualized therapy for TNBC patients.