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

Eun Cho; Hye Jin Baek; Filip Szczepankiewicz; Hyo Jung An; Eun Jung Jung; Ho-Joon Lee; Joonsung Lee; Sung-Min Gho

doi:10.21037/qims-21-870

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

Quant Imaging Med Surg. 2022 Mar;12(3):2002-2017. doi: 10.21037/qims-21-870.

Authors

Eun Cho¹, Hye Jin Baek^{1

2}, Filip Szczepankiewicz³, Hyo Jung An⁴, Eun Jung Jung⁵, Ho-Joon Lee⁶, Joonsung Lee⁷, Sung-Min Gho⁸

Affiliations

¹ Department of Radiology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Seongsan-gu, Changwon, Republic of Korea.
² Department of Radiology, Institute of Health Sciences, Gyeongsang National University School of Medicine, Jinju-daero, Jinju, Republic of Korea.
³ Department of Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Klinikgatan, Sweden.
⁴ Department of Pathology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Seongsan-gu, Changwon, Republic of Korea.
⁵ Department of Surgery, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea.
⁶ Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-gu, Busan, Republic of Korea.
⁷ GE Healthcare Korea, Seoul, Republic of Korea.
⁸ MR Clinical Solutions & Research Collaborations, GE Healthcare, Seoul, Republic of Korea.

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 tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MK_T), anisotropic mean kurtosis (MK_A), isotropic mean kurtosis (MK_I), 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, MK_T, MK_A, MK_I, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MK_I was positively correlated with the tumor size on both MRI and pathological specimen (r_s=0.38, P<0.05 vs. r_s=0.54, P<0.01), whereas MK_T had a positive correlation with the tumor size in the pathological specimen only (r_s=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MK_T, MK_A, 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.

Keywords: Magnetic resonance imaging (MRI); breast; breast cancer; diffusion-weighted imaging (DWI); invasive ductal carcinoma (IDC); tensor-valued diffusion encoding.