Quantitative differentiation of breast lesions at 3T diffusion-weighted imaging (DWI) using the ratio of distributed diffusion coefficient (DDC).
To investigate the accuracy of diffusion coefficients and diffusion coefficient ratios of breast lesions and of glandular breast tissue from mono- and stretched-exponential models for quantitative diagnosis in diffusion-weighted magnetic resonance imaging (MRI).



We analyzed pathologically confirmed 170 lesions (85 benign and 85 malignant) imaged using a 3.0T MR scanner. Small regions of interest (ROIs) focusing on the highest signal intensity for lesions and also for glandular tissue of contralateral breast were obtained. Apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were estimated by performing nonlinear fittings using mono- and stretched-exponential models, respectively. Coefficient ratios were calculated by dividing the lesion coefficient by the glandular tissue coefficient.



A stretched exponential model provides significantly better fits then the monoexponential model (P < 0.001): 65% of the better fits for glandular tissue and 71% of the better fits for lesion. High correlation was found in diffusion coefficients (0.99-0.81 and coefficient ratios (0.94) between the models. The highest diagnostic accuracy was found by the DDC ratio (area under the curve [AUC] = 0.93) when compared with lesion DDC, ADC ratio, and lesion ADC (AUC = 0.91, 0.90, 0.90) but with no statistically significant difference (P > 0.05). At optimal thresholds, the DDC ratio achieves 93% sensitivity, 80% specificity, and 87% overall diagnostic accuracy, while ADC ratio leads to 89% sensitivity, 78% specificity, and 83% overall diagnostic accuracy.



The stretched exponential model fits better with signal intensity measurements from both lesion and glandular tissue ROIs. Although the DDC ratio estimated by using the model shows a higher diagnostic accuracy than the ADC ratio, lesion DDC, and ADC, it is not statistically significant. J. Magn. Reson. Imaging 2016;44:1633-1641.
Department of Biomedical Engineering, Yeditepe University, Istanbul, Turkey.
Gokhan
G
Ertas
Institute of Diagnostic and Interventional Radiology, Oberlausitz-Kliniken gGmbH, Bautzen, Germany.
Can
C
Onaygil
Department of Radiology, Sanliurfa Mehmet Akif Inan Education and Research Hospital, Sanliurfa, Turkey.
Yasin
Y
Akin
Department of Pathology, Marmara University School of Medicine, Istanbul, Turkey.
Handan
H
Kaya
Department of Radiology, Marmara University School of Medicine, Istanbul, Turkey.
Erkin
E
Aribal
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult
apparent diffusion coefficient; breast; diffusion-weighted imaging; distributed diffusion coefficient; magnetic resonance imaging; ratio