Monoexponential, Biexponential, and stretched‐exponential models using diffusion‐weighted imaging: A quantitative differentiation of breast lesions at 3.0T

Abstract

Background Diffusion‐weighted imaging (DWI) plays an important role in the differentiation of malignant and benign breast lesions. Purpose To investigate the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched‐exponential DWI models in the differential diagnosis of breast lesions. Study Type Prospective. Population Sixty‐one patients (age range: 25–68 years old; mean age: 46 years old) with 31 malignant lesions, 42 benign lesions, and 28 normal breast tissues diagnosed initially by clinical palpation, ultrasonography, or conventional mammography were enrolled in the study from January to September 2016. Field Strength 3.0T MR scanner, T1WI, T2WI, DWI (conventional and multi‐b values), dynamic contrast‐enhanced. Assessment The apparent diffusion coefficient (ADC) was calculated by monoexponential analysis. The diffusion coefficient (ADCslow), pseudodiffusion coefficient (ADCfast), and perfusion fraction (f) were calculated using the biexponential model. The distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) were obtained using a stretched‐exponential model. All parameters were compared for malignant tumors, benign tumors, and normal breast tissues. A receiver operating characteristic curve was used to compare the ability of these parameters, in order to differentiate benign and malignant breast lesions. Statistical Tests All statistical analyses were performed using statistical software (SPSS). Results ADC, ADCslow, f, DDC, and α values were significantly lower in malignant tumors when compared with normal breast tissues and benign tumors (P < 0.05). However, ADC and f had higher area under the receiver operating characteristic curve (AUC) values (0.889 and 0.919, respectively). Data Conclusion The parameters derived from the biexponential and stretched‐exponential DWI could provide additional information for differentiating between benign and malignant breast tumors when compared with conventional diffusion parameters. Level of Evidence: 4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;50:1461–1467.

Whole Volume Apparent Diffusion Coefficient (ADC) Histogram as a Quantitative Imaging Biomarker to Differentiate Breast Lesions: Correlation with the Ki-67 Proliferation Index

“…Axial 3D fat-saturated T1WI were obtained immediately before contrast administration and at six consecutive time points following the administration of the Gd-DTPA contrast agent, with the following parameters: TR/TE, 5.1/2.1 msec; flip angle, 10; field of view, 320 × 320 mm 2 ; matrix, 400 × 70; and slice thickness, 2.4 mm. ADC maps were generated with a monoexponential fit for the diffusion data with b values of 50 and 800 sec/mm 2 using the following formula: ADC = [lnS0 − lnS( b )]/ b (where S0 and S( b ) represent the DWI signal intensity at b = 50 and 800 sec/mm 2 , respectively [ 20 , 21 ]). EPI (fat-suppressed single-shot spin-echo echo-planar imaging) was used for fat suppression.…”

Differentiation of Prostate Cancer and Stromal Hyperplasia in the Transition Zone With Monoexponential, Stretched-Exponential Diffusion-Weighted Imaging and Diffusion Kurtosis Imaging in a Reduced Number of b Values: Correlation With Whole-Mount Pathology

“…The DDC and α values of SEM in our study were also significantly lower in TZ cancer than in SH tissue, which corresponded to previous studies. 15,16 In the SEM, DDC and α were used to evaluate the water diffusion characteristics. Distributed diffusion coefficient can be regarded as a weighted sum of the ADC distribution, representing the mean intravoxel diffusion rates.…”

Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Quantitative Differentiation of Breast Tumors: A Meta-Analysis

“…However, the diagnostic performance of IVIM-DWIderived parameters in the differentiation of breast tumors is not consistent, and the application of this sequence remains debatable. For example, several studies (7,11,12) indicated that breast cancer had a higher D * value than did benign lesions, while other studies reported adverse (10,(13)(14)(15) or non-significant results (9,16,17). The studies of Cho et al (9) and Lin et al (13) suggested that the IVIM model can further distinguish invasive ductal carcinoma (IDC) from ductal carcinoma in situ (DCIS), while another study reported no significant difference in the D, D * , and f values between them (7).…”