Triexponential Diffusion Analysis of Diffusion-weighted Imaging for Breast Ductal Carcinoma in Situ and Invasive Ductal Carcinoma

Masako Ohno, Naoki Ohno, Tosiaki Miyati, Hiroko Kawashima, Kazuto Kozaka, Yukihiro Matsuura, Toshifumi Gabata, Satoshi Kobayashi
2021 Magnetic Resonance in Medical Sciences  
To obtain detailed information in breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) using triexponential diffusion analysis. Diffusion-weighted images (DWI) of the breast were obtained using single-shot diffusion echo-planar imaging with 15 b-values. Mean signal intensities at each b-value were measured in the DCIS and IDC lesions and fitted with the triexponential function based on a two-step approach: slow-restricted diffusion coefficient (Ds) was initially determined
more » ... nitially determined using a monoexponential function with b-values > 800 s/mm2. The diffusion coefficient of free water at 37°C was assigned to the fast-free diffusion coefficient (Df). Finally, the perfusion-related diffusion coefficient (Dp) was derived using all the b-values. Furthermore, biexponential analysis was performed to obtain the perfusion-related diffusion coefficient (D*) and the perfusion-independent diffusion coefficient (D). Monoexponential analysis was performed to obtain the apparent diffusion coefficient (ADC). The sensitivity and specificity of the aforementioned diffusion coefficients for distinguishing between DCIS and IDC were evaluated using the pathological results. The Ds, D, and ADC of DCIS were significantly higher than those of IDC (P < 0.01 for all). There was no significant correlation between Dp and Ds, but there was a weak correlation between D* and D. The combination of Dp and Ds showed higher sensitivity and specificity (85.9% and 71.4%, respectively), compared to the combination of D* and D (81.5% and 33.3%, respectively). Triexponential analysis can provide detailed diffusion information for breast tumors that can be used to differentiate between DCIS and IDC.
doi:10.2463/mrms.mp.2020-0103 pmid:33563872 fatcat:scslkp5g7zd3vlqcyl22cetbum