Multidimensional diffusion magnetic resonance imaging for characterization of tissue microstructure in breast cancer patients : A prospective pilot study
Naranjo, Isaac Daimiel ; Reymbaut, Alexis LU ; Brynolfsson, Patrik LU
; Gullo, Roberto Lo
; Bryskhe, Karin
LU
; Topgaard, Daniel
LU
; Giri, Dilip D.
; Reiner, Jeffrey S.
; Thakur, Sunitha B.
and Pinker-Domenig, Katja
(2021)
In Cancers
13(7).
- 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... (More)
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.
(Less)
- author
- Naranjo, Isaac Daimiel
; Reymbaut, Alexis
LU
; Brynolfsson, Patrik
LU
; Gullo, Roberto Lo
; Bryskhe, Karin
LU
; Topgaard, Daniel
LU
; Giri, Dilip D.
; Reiner, Jeffrey S.
; Thakur, Sunitha B.
and Pinker-Domenig, Katja
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Breast cancer, Diffusion-weighted imaging, Magnetic resonance imaging, Multidimensional diffusion MRI, Oscillating gradients
- in
- Cancers
- volume
- 13
- issue
- 7
- article number
- 1606
- publisher
- MDPI AG
- external identifiers
-
- scopus:85103338419
- pmid:33807205
- ISSN
- 2072-6694
- DOI
- 10.3390/cancers13071606
- language
- English
- LU publication?
- yes
- id
- bc054fb9-5461-45e6-8e38-6047b8a8fa20
- date added to LUP
- 2021-04-07 09:48:49
- date last changed
- 2024-06-15 09:21:05
@article{bc054fb9-5461-45e6-8e38-6047b8a8fa20,
abstract = {{<p>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<sup>−3</sup> mm<sup>2</sup>/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<sup>−3</sup> mm<sup>2</sup>/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.</p>}},
author = {{Naranjo, Isaac Daimiel and Reymbaut, Alexis and Brynolfsson, Patrik and Gullo, Roberto Lo and Bryskhe, Karin and Topgaard, Daniel and Giri, Dilip D. and Reiner, Jeffrey S. and Thakur, Sunitha B. and Pinker-Domenig, Katja}},
issn = {{2072-6694}},
keywords = {{Breast cancer; Diffusion-weighted imaging; Magnetic resonance imaging; Multidimensional diffusion MRI; Oscillating gradients}},
language = {{eng}},
number = {{7}},
publisher = {{MDPI AG}},
series = {{Cancers}},
title = {{Multidimensional diffusion magnetic resonance imaging for characterization of tissue microstructure in breast cancer patients : A prospective pilot study}},
url = {{http://dx.doi.org/10.3390/cancers13071606}},
doi = {{10.3390/cancers13071606}},
volume = {{13}},
year = {{2021}},
}