Automated estimation of tumor probability in prostate magnetic resonance spectroscopic imaging: Pattern recognition vs quantification
- 26 December 2006
- journal article
- research article
- Published by Wiley in Magnetic Resonance in Medicine
- Vol. 57 (1), 150-159
- https://doi.org/10.1002/mrm.21112
Abstract
Despite its diagnostic value and technological availability, 1H NMR spectroscopic imaging (MRSI) has not found its way into clinical routine yet. Prerequisite for the clinical application is an automated and reliable method for the diagnostic evaluation of MRS images. In the present paper, different approaches to the estimation of tumor probability from MRSI in the prostate are assessed. Two approaches to feature extraction are compared: quantification (VARPRO, AMARES, QUEST) and subspace methods on spectral patterns (principal components, independent components, nonnegative matrix factorization, partial least squares). Linear as well as nonlinear classifiers (support vector machines, Gaussian processes, random forests) are applied and discussed. Quantification‐based approaches are much more sensitive to the choice and parameterization of the quantification algorithm than to the choice of the classifier. Furthermore, linear methods based on magnitude spectra easily achieve equal performance and also allow for biochemical interpretation in combination with subspace methods. Nonlinear methods operating directly on magnitude spectra achieve the best results but are less transparent than the linear methods. Magn Reson Med 57:150–159, 2007.This publication has 30 references indexed in Scilit:
- Optimal classification of long echo timein vivo magnetic resonance spectra in the detection of recurrent brain tumorsNMR in Biomedicine, 2006
- Tissue segmentation and classification of MRSI data using canonical correlation analysisMagnetic Resonance in Medicine, 2005
- Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classificationNMR in Biomedicine, 2005
- Nonnegative Matrix Factorization for Rapid Recovery of Constituent Spectra in Magnetic Resonance Chemical Shift Imaging of the BrainIEEE Transactions on Medical Imaging, 2004
- Classification of brain tumours using short echo time 1H MR spectraJournal of Magnetic Resonance, 2004
- Fast acquisition-weighted three-dimensional proton MR spectroscopic imaging of the human prostateMagnetic Resonance in Medicine, 2004
- Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant AnalysisNeural Computation, 2002
- Generalized Linear Regression on Sampled Signals and Curves: A P-Spline ApproachTechnometrics, 1999
- Iteratively Reweighted Partial Least Squares Estimation for Generalized Linear RegressionTechnometrics, 1996
- SVD-based quantification of magnetic resonance signalsJournal of Magnetic Resonance (1969), 1992