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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
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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
Summary
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.
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.
