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Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer

Cho, Eun ; Baek, Hye Jin ; Szczepankiewicz, Filip LU orcid ; An, Hyo Jung ; Jung, Eun Jung ; Lee, Ho Joon ; Lee, Joonsung and Gho, Sung Min (2022) In Quantitative Imaging in Medicine and Surgery 12(3). p.2002-2017
Abstract

Background: Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting. Methods: We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by... (More)

Background: Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting. Methods: We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (μFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items. Results: The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and μFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and μFA compared to the metastasis negative group (P<0.05). Conclusions: Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, μFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Breast, Breast cancer, Diffusion-weighted imaging (DWI), Invasive ductal carcinoma (IDC), Magnetic resonance imaging (MRI), Tensor-valued diffusion encoding
in
Quantitative Imaging in Medicine and Surgery
volume
12
issue
3
pages
16 pages
publisher
AME Publishing Company
external identifiers
  • pmid:35284250
  • scopus:85122859423
ISSN
2223-4292
DOI
10.21037/qims-21-870
language
English
LU publication?
yes
id
a17cd697-2e27-4347-bab5-ef0979048488
date added to LUP
2022-03-02 13:51:17
date last changed
2025-03-21 15:34:24
@article{a17cd697-2e27-4347-bab5-ef0979048488,
  abstract     = {{<p>Background: Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting. Methods: We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (μFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items. Results: The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P&lt;0.001); however, MKT, MKA, MKI, FA, and μFA were significantly higher (P&lt;0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P&lt;0.05 vs. rs=0.54, P&lt;0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P&lt;0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and μFA compared to the metastasis negative group (P&lt;0.05). Conclusions: Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, μFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.</p>}},
  author       = {{Cho, Eun and Baek, Hye Jin and Szczepankiewicz, Filip and An, Hyo Jung and Jung, Eun Jung and Lee, Ho Joon and Lee, Joonsung and Gho, Sung Min}},
  issn         = {{2223-4292}},
  keywords     = {{Breast; Breast cancer; Diffusion-weighted imaging (DWI); Invasive ductal carcinoma (IDC); Magnetic resonance imaging (MRI); Tensor-valued diffusion encoding}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{2002--2017}},
  publisher    = {{AME Publishing Company}},
  series       = {{Quantitative Imaging in Medicine and Surgery}},
  title        = {{Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer}},
  url          = {{http://dx.doi.org/10.21037/qims-21-870}},
  doi          = {{10.21037/qims-21-870}},
  volume       = {{12}},
  year         = {{2022}},
}