This paper discusses the potential of Multivariate Curve Resolution (MCR) models to extract physiological dynamics behaviors from Dynamic Contrast Enhanced Magnetic Resonance (DCE-MR) Imaging prostate perfusion studies for cancer diagnosis. A relationship with biomarkers ( hidden parameters for assessing the possible existence of a tumor) from pharmacokinetic models is also studied.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Prats-Montalbán, JM.; Sanz Requena, R.; Marti Bonmati, L.; Ferrer, A. (2014). Prostate functional magnetic resonance image analysis using multivariate curve resolution methods. Journal of Chemometrics. 28(8):672-680. https://doi.org/10.1002/cem.2585S672680288Collins, D. J., & Padhani, A. R. (2004). Dynamic magnetic resonance imaging of tumor perfusion. IEEE Engineering in Medicine and Biology Magazine, 23(5), 65-83. doi:10.1109/memb.2004.1360410JACKSON, A. S. N., REINSBERG, S. A., SOHAIB, S. A., CHARLES-EDWARDS, E. M., JHAVAR, S., CHRISTMAS, T. J., … DEARNALEY, D. P. (2009). Dynamic contrast-enhanced MRI for prostate cancer localization. The British Journal of Radiology, 82(974), 148-156. doi:10.1259/bjr/89518905Leach, M. O., Brindle, K. M., Evelhoch, J. L., Griffiths, J. R., Horsman, M. R., Jackson, A., … Workman, P. (2005). The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. British Journal of Cancer, 92(9), 1599-1610. doi:10.1038/sj.bjc.6602550Tofts, P. S., Brix, G., Buckley, D. L., Evelhoch, J. L., Henderson, E., Knopp, M. V., … Weisskoff, R. M. (1999). Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: Standardized quantities and symbols. Journal of Magnetic Resonance Imaging, 10(3), 223-232. doi:10.1002/(sici)1522-2586(199909)10:33.0.co;2-sPort, R. E., Knopp, M. V., & Brix, G. (2001). Dynamic contrast-enhanced MRI using Gd-DTPA: Interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors. Magnetic Resonance in Medicine, 45(6), 1030-1038. doi:10.1002/mrm.1137McGrath, D. M., Bradley, D. P., Tessier, J. L., Lacey, T., Taylor, C. J., & Parker, G. J. M. (2009). Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats. Magnetic Resonance in Medicine, 61(5), 1173-1184. doi:10.1002/mrm.21959Yang, C., Karczmar, G. S., Medved, M., Oto, A., Zamora, M., & Stadler, W. M. (2009). Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced-MRI analysis. Magnetic Resonance in Medicine, 61(4), 851-859. doi:10.1002/mrm.21912Meng, R., Chang, S. D., Jones, E. C., Goldenberg, S. L., & Kozlowski, P. (2010). Comparison between Population Average and Experimentally Measured Arterial Input Function in Predicting Biopsy Results in Prostate Cancer. Academic Radiology, 17(4), 520-525. doi:10.1016/j.acra.2009.11.006Sourbron, S. P., & Buckley, D. L. (2011). Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Physics in Medicine and Biology, 57(2), R1-R33. doi:10.1088/0031-9155/57/2/r1Lüdemann, L., Prochnow, D., Rohlfing, T., Franiel, T., Warmuth, C., Taupitz, M., … Beyersdorff, D. (2009). Simultaneous Quantification of Perfusion and Permeability in the Prostate Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging with an Inversion-Prepared Dual-Contrast Sequence. Annals of Biomedical Engineering, 37(4), 749-762. doi:10.1007/s10439-009-9645-xPrats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Jackson, J. E. (1991). A Use’s Guide to Principal Components. Wiley Series in Probability and Statistics. doi:10.1002/0471725331Bruwer, M.-J., MacGregor, J. F., & Noseworthy, M. D. (2008). Dynamic contrast-enhanced MRI diagnostics in oncology via principal component analysis. Journal of Chemometrics, 22(11-12), 708-716. doi:10.1002/cem.1143Gujral, P., Amrhein, M., Bonvin, D., Vallée, J.-P., Montet, X., & Michoux, N. (2009). Classification of magnetic resonance images from rabbit renal perfusion. Chemometrics and Intelligent Laboratory Systems, 98(2), 173-181. doi:10.1016/j.chemolab.2009.06.004Fortuna, J., Elzibak, A. H., Fan, Z., MacGregor, J. F., & Noseworthy, M. D. (2012). Liver functional magnetic resonance imaging analysis using a latent variables approach. Journal of Chemometrics, 26(5), 170-179. doi:10.1002/cem.2430Buckley, D. L. (2002). Uncertainty in the analysis of tracer kinetics using dynamic contrast-enhancedT1-weighted MRI. Magnetic Resonance in Medicine, 47(3), 601-606. doi:10.1002/mrm.10080Henderson, E., Sykes, J., Drost, D., Weinmann, H.-J., Rutt, B. K., & Lee, T.-Y. (2000). Simultaneous MRI measurement of blood flow, blood volume, and capillary permeability in mammary tumors using two different contrast agents. Journal of Magnetic Resonance Imaging, 12(6), 991-1003. doi:10.1002/1522-2586(200012)12:63.0.co;2-1Tauler, R., Smilde, A., & Kowalski, B. (1995). Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution. Journal of Chemometrics, 9(1), 31-58. doi:10.1002/cem.1180090105Tauler, R. (1995). Multivariate curve resolution applied to second order data. Chemometrics and Intelligent Laboratory Systems, 30(1), 133-146. doi:10.1016/0169-7439(95)00047-xPiqueras, S., Duponchel, L., Tauler, R., & de Juan, A. (2011). Resolution and segmentation of hyperspectral biomedical images by Multivariate Curve Resolution-Alternating Least Squares. Analytica Chimica Acta, 705(1-2), 182-192. doi:10.1016/j.aca.2011.05.020De Juan, A., & Tauler, R. (2003). Chemometrics applied to unravel multicomponent processes and mixtures. Analytica Chimica Acta, 500(1-2), 195-210. doi:10.1016/s0003-2670(03)00724-4Jaumot, J., Gargallo, R., de Juan, A., & Tauler, R. (2005). A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB. Chemometrics and Intelligent Laboratory Systems, 76(1), 101-110. doi:10.1016/j.chemolab.2004.12.007Windig, W., & Guilment, J. (1991). Interactive self-modeling mixture analysis. Analytical Chemistry, 63(14), 1425-1432. doi:10.1021/ac00014a016Gallagher, N. B., Shaver, J. M., Martin, E. B., Morris, J., Wise, B. M., & Windig, W. (2004). Curve resolution for multivariate images with applications to TOF-SIMS and Raman. Chemometrics and Intelligent Laboratory Systems, 73(1), 105-117. doi:10.1016/j.chemolab.2004.04.003Windig, W. (2009). Two-Way Data Analysis: Detection of Purest Variables. Comprehensive Chemometrics, 275-307. doi:10.1016/b978-044452701-1.00048-xChtioui, Y., Bertrand, D., & Barba, D. (1998). Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision. Journal of the Science of Food and Agriculture, 76(1), 77-86. doi:10.1002/(sici)1097-0010(199801)76:13.0.co;2-9Wang, M., Zhou, X., King, R. W., & Wong, S. T. (2007). BMC Bioinformatics, 8(1), 32. doi:10.1186/1471-2105-8-32Sánchez, F. C., Toft, J., van den Bogaert, B., & Massart, D. L. (1996). Orthogonal Projection Approach Applied to Peak Purity Assessment. Analytical Chemistry, 68(1), 79-85. doi:10.1021/ac950496gMultivariate curve resolution homepage http://www.mcrals.info/De Juan, A., Maeder, M., Hancewicz, T., & Tauler, R. (2005). Local rank analysis for exploratory spectroscopic image analysis. Fixed Size Image Window-Evolving Factor Analysis. Chemometrics and Intelligent Laboratory Systems, 77(1-2), 64-74. doi:10.1016/j.chemolab.2004.11.006Keller, H. R., & Massart, D. L. (1991). Peak purity control in liquid chromatography with photodiode-array detection by a fixed size moving window evolving factor analysis. Analytica Chimica Acta, 246(2), 379-390. doi:10.1016/s0003-2670(00)80976-9De Juan, A., Maeder, M., Hancewicz, T., Duponchel, L., & Tauler, R. (s. f.). Chemometric Tools for Image Analysis. Infrared and Raman Spectroscopic Imaging, 65-109. doi:10.1002/9783527628230.ch2De Juan, A., Maeder, M., Hancewicz, T., & Tauler, R. (2008). Use of local rank-based spatial information for resolution of spectroscopic images. Journal of Chemometrics, 22(5), 291-298. doi:10.1002/cem.1099Orton, M. R., d’ Arcy, J. A., Walker-Samuel, S., Hawkes, D. J., Atkinson, D., Collins, D. J., & Leach, M. O. (2008). Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. Physics in Medicine and Biology, 53(5), 1225-1239. doi:10.1088/0031-9155/53/5/005Parker, G. J. M., Roberts, C., Macdonald, A., Buonaccorsi, G. A., Cheung, S., Buckley, D. L., … Jayson, G. C. (2006). Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 56(5), 993-1000. doi:10.1002/mrm.21066Jaumot, J., & Tauler, R. (2010). MCR-BANDS: A user friendly MATLAB program for the evaluation of rotation ambiguities in Multivariate Curve Resolution. Chemometrics and Intelligent Laboratory Systems, 103(2), 96-107. doi:10.1016/j.chemolab.2010.05.02
