Prostate Cancer Detection With Multi-parametric MRI: Logistic Regression Analysis of Quantitative T2, Diffusion-Weighted Imaging, and Dynamic Contrast-Enhanced MRI

Purpose: To develop a multi-parametric model suitable for prospectively identifying prostate cancer in peripheral zone (PZ) using magnetic resonance imaging (MRI). Materials and Methods: Twenty-five radical prostatectomy patients (median age, 63 years; range, 44-72 years) had T2-weighted, diffusion-weighted imaging (DWI), T2-mapping, and dynamic contrast-enhanced (DCE) MRI at 1.5 Tesla (T) with endorectal coil to yield parameters apparent diffusion coefficient (ADC), T2, volume transfer constant (K-trans) and extravascular extracellular volume fraction (v(e)). Whole-mount histology was generated from surgical specimens and PZ tumors delineated. Thirty-eight tumor outlines, one per tumor, and pathologically normal PZ regions were transferred to MR images. Receiver operating characteristic (ROC) curves were generated using all identified normal and tumor voxels. Step-wise logistic-regression modeling was performed, testing changes in deviance for significance. Areas under the ROC curves (A(z)) were used to evaluate and compare performance. Results: The best-performing single-parameter was ADC (mean A(z) [95% confidence interval]: A(z,ADC): 0.689 [0.675, 0.702]; A(z.T2): 0.673 [0.659, 0.687]; A(z,Ktrans): 0.592 [0.578, 0.606]; A(z,ve): 0. 543 [0.528, 0.557]). The optimal multi-parametric model, LR-3p, consisted of combining ADC, T2 and K-trans Mean A(z,LR-3p) was 0.706 [0.692, 0.719], which was significantly higher than A(z,T2), A(z,Ktrans), and A(z,ve) (P < 0.002). A(z,LR-3p) tended to be greater than A(z,ADC), however, this result was not statistically significant (P = 0.090). Conclusion: Using logistic regression, an objective model capable of mapping PZ tumor with reasonable performance can be constructed.