Meltem Uyanik^{1}, Michael Abern^{2}, Brandon Caldwell^{2}, Muge Karaman^{3}, Winnie Mar^{4}, Virgilia Macias^{5}, Xiaohong Joe Zhou^{1,3,4,6}, and Richard Magin^{7}

Prostate cancer is a common malignancy among men. Using MRI to discriminating high-grade disease from benign and indolent cancer in the prostate is highly desirable for treatment planning. Single and multi- exponential models of diffusion signal decay in the prostate has proven useful for determining prostate cancer tissue structure. However, classification of cancer grade remains illusive. In this study, we investigate the stretched exponential signal decay model using histology and ROC analysis to determine if it will more accurately characterize aggressive prostate cancer.

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(a) The prostate gland is in an
axial DW normalized MR image (b= 50 s/mm2) at the mid-base region of the
prostate. (b) Annotated prostate at approximate level of MRI. Prospectively,
this will be done in true whole mount and with a pure two-color scheme.

Maps of ADC, DDC, and α maps from a representative patient. ADC map (mm2/s ^10-3) is fitted to the mono-exponential
model. DDC (mm2/s^10-3) and α maps are fitted to the stretched-exponential model. FOV: 7 ×7 cm2.

(a) Boxplots of the mean values of the stretched exponential
model parameters (ADC, DDC, and α). (b) The corresponding
descriptive statistics, showing sample mean and standard deviation, (±σ), of ADC, DDC, and α for healthy, and
unhealthy groups. All parameters are exhibiting significant differences (p-values<0.05).

(a) The ROC curve of
using the ADC, DDC, α, and combined (DDC,α) for characterizing
aggressive prostate cancer. (b) Summary of the corresponding best cut-off sensitivity and specificity
values (shown as circles in the curves) as well as the accuracy and the AUC for
the ROC curves. The combination of stretched exponential model the parameters
were obtained by using a multivariate logistic regression algorithm.