Prostate cancer detection with multi‐parametric MRI: Logistic regression analysis of quantitative T2, diffusion‐weighted imaging,and dynamic contrast‐enhanced MRI |
| |
| Authors: | Deanna L. Langer MSc Theodorus H. van der Kwast PhD MD Andrew J. Evans PhD MD John Trachtenberg MD CM Brian C. Wilson PhD Masoom A. Haider MD |
| |
| Affiliation: | 1. Department of Medical Imaging, Princess Margaret Hospital, University Health Network and Mount Sinai Hospital, Toronto, Ontario, Canada;2. Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada;3. Department of Pathology and Laboratory Medicine, Toronto General Hospital, University Health Network, Ontario, Canada;4. Department of Surgical Oncology, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada;5. Department of Medical Biophysics, University of Toronto, Ontario Cancer Institute, Toronto, Ontario, Canada |
| |
| Abstract: | 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 (Ktrans) and extravascular extracellular volume fraction (ve). 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 (Az) were used to evaluate and compare performance. Results The best‐performing single‐parameter was ADC (mean Az [95% confidence interval]: Az,ADC: 0.689 [0.675, 0.702]; Az,T2: 0.673 [0.659, 0.687]; Az,Ktrans: 0.592 [0.578, 0.606]; Az,ve: 0.543 [0.528, 0.557]). The optimal multi‐parametric model, LR‐3p, consisted of combining ADC, T2 and Ktrans. Mean Az,LR‐3p was 0.706 [0.692, 0.719], which was significantly higher than Az,T2, Az,Ktrans, and Az,ve (P < 0.002). Az,LR‐3p tended to be greater than Az,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. J. Magn. Reson. Imaging 2009;30:327–334. © 2009 Wiley‐Liss, Inc. |
| |
| Keywords: | multi‐parametric MRI prostate cancer quantitative T2 diffusion weighted imaging (DWI) dynamic contrast enhanced MRI (DCE‐MRI) |
|
|
