title: Pharmacokinetic analysis of prostate cancer using independent component analysis.
abstract: Dynamic contrast enhanced (DCE)-MRI combined with pharmacokinetic (PK) modeling of a tumor provides information about its perfusion and vascular permeability. Most PK models require the time course of contrast agent concentration in blood plasma as an input, which cannot be measured directly at the tissue of interest, and is approximated with an arterial input function (AIF). Variability in methods used in estimating the AIF and inter-observer variability in region of interest selection are major sources of discrepancy between different studies. This study had two aims. The first was to determine whether a local vascular input function (VIF) estimated using an adaptive complex independent component analysis (AC-ICA) algorithm could be used to estimate PK parameters from clinical dynamic contrast enhanced (DCE)-MRI studies. The second aim was to determine whether normalizing the input function using its area under the curve would improve the results of PK analysis. AC-ICA was applied to DCE-MRI of 27 prostate cancer patients and the intravascular signal was estimated. This signal was converted into contrast agent concentration to give a local vascular input function (VIF) which was used as the input function for PK analysis. We compared K(trans) values for normal peripheral zone (PZ) and tumor tissues using the local VIF with those calculated using a conventional AIF obtained from the femoral artery. We also compared the K(trans) values obtained from the un-normalized input functions with the KN(trans) values obtained after normalizing the AIF and local VIF. Normalization of the input function resulted in smaller variation in PK parameters (KN(trans) vs. K(trans) for normal PZ tissue was 0.20±0.04mM.min(-1) vs. 0.87±0.54min(-1) for local VIF and 0.21±0.07mM.min(-1) vs. 0.25±0.29min(-1) for AIF) and better separation of the normal and tumor tissues (effect-size of this separation using KN(trans) vs. K(trans) was 0.89 vs. 0.75 for local VIF and 0.94 vs. 0.41 for AIF). The AC-ICA and AIF-based analyses provided similar (KN(trans)) values in normal PZ tissue of prostate across patients. Normalizing the input function before PK analysis significantly improved the reproducibility of the PK parameters and increased the separation between normal and tumor tissues. Using AC-ICA allows a local VIF to be estimated and the resulting PK parameters are similar to those obtained using a more conventional AIF; this may be valuable in studies where an artery is not available in the field of view.
authors: Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Department of Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada. Electronic address: hatef.mehrabian@sri.utoronto.ca.
authors: Hatef
authors: H
authors: Mehrabian
authors: Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
authors: Michael
authors: M
authors: Da Rosa
authors: Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
authors: Masoom A
authors: MA
authors: Haider
authors: Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Department of Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.
authors: Anne L
authors: AL
authors: Martel
mesh_terms: D000368:Aged; D000465:Algorithms; D003287:Contrast Media / Q000493:pharmacokinetics*; D019786:Gadolinium DTPA / Q000493:pharmacokinetics*; D006801:Humans; D007089:Image Enhancement; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA
keywords: Adaptive complex ICA; Dynamic contrast enhanced (DCE)-MRI; Independent component analysis (ICA); Pharmacokinetic modeling; Prostate cancer