title: A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI.
abstract: Magnetic resonance imaging (MRI), particularly dynamic contrast enhanced (DCE) imaging, has shown great potential in prostate cancer diagnosis and staging. In the current practice of DCE-MRI, diagnosis is based on quantitative parameters extracted from the series of T1-weighted images acquired after the injection of a contrast agent. To calculate these parameters, a pharmacokinetic model is fitted to the T1-weighted intensities. Most models make simplistic assumptions about the perfusion process. Moreover, these models require accurate estimation of the arterial input function, which is challenging. In this work we propose a data-driven approach to characterization of the prostate tissue that uses the time series of DCE T1-weighted images without pharmacokinetic modeling. This approach uses a number of model-free empirical parameters and also the principal component analysis (PCA) of the normalized T1-weighted intensities, as features for cancer detection from DCE MRI. The optimal set of principal components is extracted with sparse regularized regression through least absolute shrinkage and selection operator (LASSO). A support vector machine classifier was used with leave-one-patient-out cross validation to determine the ability of this set of features in cancer detection. Our data is obtained from patients prior to radical prostatectomy and the results are validated based on histological evaluation of the extracted specimens. Our results, obtained on 449 tissue regions from 16 patients, show that the proposed data-driven features outperform the traditional pharmacokinetic parameters with an area under ROC of 0.86 for LASSO-isolated PCA parameters, compared to 0.78 for pharmacokinetic parameters. This shows that our novel approach to the analysis of DCE data has the potential to improve the multiparametric MRI protocol for prostate cancer detection.
authors: University of British Columbia, Vancouver, BC, Canada.
authors: Nandinee Fariah
authors: NF
authors: Haq
authors: University of British Columbia, Vancouver, BC, Canada.
authors: Piotr
authors: P
authors: Kozlowski
authors: University of British Columbia, Vancouver, BC, Canada.
authors: Edward C
authors: EC
authors: Jones
authors: University of British Columbia, Vancouver, BC, Canada.
authors: Silvia D
authors: SD
authors: Chang
authors: University of British Columbia, Vancouver, BC, Canada.
authors: S Larry
authors: SL
authors: Goldenberg
authors: University of British Columbia, Vancouver, BC, Canada. Electronic address: moradi@ece.ubc.ca.
authors: Mehdi
authors: M
authors: Moradi
mesh_terms: D000368:Aged; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D064847:Multimodal Imaging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Dynamic contrast enhanced MRI; LASSO; PCA; Prostate cancer; SVM