Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors

Abstract

Background: The purpose of this study is to statistically assess whether bi-exponential intravoxel incoherent motion (IVIM) model better characterizes diffusion weighted imaging (DWI) signal of malignant breast tumor than mono-exponential Gaussian diffusion model.Methods: 3 T DWI data of 29 malignant breast tumors were retrospectively included. Linear least-square mono-exponential fitting and segmented least-square bi-exponential fitting were used for apparent diffusion coefficient (ADC) and IVIM parameter quantification, respectively. F-test and Akaike Information Criterion (AIC) were used to statistically assess the preference of mono-exponential and bi-exponential model using region-of-interests (ROI)-averaged and voxel-wise analysis.Results: For ROI-averaged analysis, 15 tumors were significantly better fitted by bi-exponential function and 14 tumors exhibited mono-exponential behavior. The calculated ADC, D (true diffusion coefficient) and f (pseudo-diffusion fraction) showed no significant differences between mono-exponential and biexponential preferable tumors. Voxel-wise analysis revealed that 27 tumors contained more voxels exhibiting mono-exponential DWI decay while only 2 tumors presented more bi-exponential decay voxels. ADC was consistently and significantly larger than D for both ROI-averaged and voxel-wise analysis.Conclusions: Although the presence of IVIM effect in malignant breast tumors could be suggested, statistical assessment shows that bi-exponential fitting does not necessarily better represent the DWI signal decay in breast cancer under clinically typical acquisition protocol and signal-to-noise ratio (SNR). Our study indicates the importance to statistically examine the breast cancer DWI signal characteristics in practice.

Ultrasound-guided percutaneous microwave ablation for benign breast lesions: evaluated by contrast-enhanced ultrasound combined with magnetic resonance imaging

“…So US can not accurately measure the volume of the lesion and not accurately observe the spatial distribution of lesional blood flow (13). Breast MRI is the most sensitive examination for breast tumor detection and it can be used to detect the spatial relationship between the lesion and the surrounding tissue, the spatial distribution of lesional blood flow as well (14)(15)(16)(17)(18). What's more, MRI can be used to assess tissue necrosis after ablation by administrating contrast agent (19,20).…”

Utility of intravoxel incoherent motion MRI derived parameters for prediction of aggressiveness in urothelial bladder carcinoma

“…The IVIM‐derived true diffusion coefficient (D) accurately represents the water molecular diffusion, pseudodiffusion coefficient (D*) is related to the microvascular portion and the perfusion fraction (f) reflects the fractional volume occupied in the voxel by microvascular flowing spins . Recently, several studies have investigated the utility of IVIM‐MRI in predicting pathological grading of gliomas, differential diagnosis of malignant and benign breast tumors and evaluation of the effect of chemotherapy on rectal cancers . However, there was limited study published on IVIM‐MRI in bladder cancers .…”

“…10,11 Recently, several studies have investigated the utility of IVIM-MRI in predicting pathological grading of gliomas, differential diagnosis of malignant and benign breast tumors and evaluation of the effect of chemotherapy on rectal cancers. [11][12][13] However, there was limited study published on IVIM-MRI in bladder cancers. 14 Therefore, we conducted this study to evaluate the usefulness of IVIMderived parameters in predicting aggressiveness of bladder urothelial carcinoma.…”

Orientation dependence and decay characteristics of T2* relaxation in the human meniscus studied with 7 Tesla MR microscopy and compared to histology

“…Additionally, we also used F‐tests to compare the goodness of fit of the mono‐ and biexponential model and assess, for each voxel, whether a biexponential model is more appropriate over a monoexponential model. This was evaluated by comparing the standard error of the estimate adjusted for the number of degrees of freedom (Equation ):…”

“…Additionally, we also used F-tests to compare the goodness of fit of the mono-and biexponential model and assess, for each voxel, whether a biexponential model is more appropriate over a monoexponential model. This was evaluated by comparing the standard error of the estimate adjusted for the number of degrees of freedom 35 (Equation (5)): where SSE mono and SSE bi are the standard error of the estimate (SSE) of the mono-and biexponential fits, respectively, and v mono and v bi are the degrees of freedom of both analyses (v = n -k). The P value of the associated F-ratio was then calculated based on F-distribution.…”