Multidimensional diffusion magnetic resonance imaging for characterization of tissue microstructure in breast cancer patients: A prospective pilot study Journal Article

   

Authors:	Daimiel Naranjo, I.; Reymbaut, A.; Brynolfsson, P.; Lo Gullo, R.; Bryskhe, K.; Topgaard, D.; Giri, D. D.; Reiner, J. S.; Thakur, S. B.; Pinker-Domenig, K.
Article Title:	Multidimensional diffusion magnetic resonance imaging for characterization of tissue microstructure in breast cancer patients: A prospective pilot study
Abstract:	Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of “size” (1.43 ± 0.54 × 10−3 mm2/s) and higher mean values of “shape” (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of “size” (2.33 ± 0.22 × 10−3 mm2/s) and lower mean values of “shape” (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords:	magnetic resonance imaging; breast cancer; diffusion-weighted imaging; multidimensional diffusion mri; oscillating gradients
Journal Title:	Cancers
Volume:	13
Issue:	7
ISSN:	2072-6694
Publisher:	MDPI
Date Published:	2021-04-01
Start Page:	1606
Language:	English
DOI:	10.3390/cancers13071606
PROVIDER:	scopus
PMCID:	PMC8037718
PUBMED:	33807205
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103338419&doi=10.3390%2fcancers13071606&partnerID=40&md5=77c828d720a132ae2681564d4f53187f
Notes:	Article -- Export Date: 3 May 2021 -- Source: Scopus

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