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Diffusion-weighted imaging reflects variable cellularity and stromal density present in breast fibroadenomas.

Sana Parsian¹, Nadia V Giannakopoulos², Habib Rahbar¹

¹Department of Radiology, Seattle Cancer Care Alliance, University of Washington School of Medicine, 825 Eastlake Ave. E., G3-200, Seattle, WA 98109-1023, USA.

Clinical Imaging

|July 6, 2016

Summary

Histopathologic features like cellularity and stroma influence apparent diffusion coefficient (ADC) values in breast fibroadenomas. These factors may explain the varied ADC measurements seen in these benign breast lesions.

Keywords:

Apparent diffusion coefficient (ADC)Cellularity Diffusion-weighted imaging (DWI)Fibroadenomas Stroma

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Area of Science:

Radiology
Pathology
Oncology

Background:

Breast fibroadenomas are common benign breast lesions.
Apparent diffusion coefficient (ADC) values derived from diffusion-weighted magnetic resonance imaging (MRI) can aid in characterizing breast lesions.
Variability in ADC values for fibroadenomas can complicate interpretation.

Purpose of the Study:

To investigate the histopathologic features of breast fibroadenomas that correlate with apparent diffusion coefficient (ADC) values.
To understand the factors contributing to the wide range of ADC measures in fibroadenomas.

Main Methods:

Retrospective analysis of 26 biopsy-proven fibroadenomas initially identified as suspicious on MRI.
Comparison of histopathologic assessments (lesion cellularity, stromal type) with ADC values from diffusion-weighted MRI.

Main Results:

Increased epithelial cellularity (epithelial hyperplasia) was significantly associated with lower ADC values (P=.02).
Dense collagenous stroma was also significantly associated with lower ADC values (P=.004).

Conclusions:

Histopathologic characteristics, specifically epithelial cellularity and stromal composition, significantly impact breast fibroadenoma ADC values.
These findings help explain the observed heterogeneity in ADC measurements for benign fibroadenomas.
Understanding these correlations may improve the diagnostic accuracy of MRI in characterizing breast lesions.