Prostate Diffusion Weighted-Magnetic Resonance Image analysis using Multivariate Curve Resolution methods

[EN] Multivariate Curve Resolution (MCR) has been applied on prostate Diffusion Weighted-Magnetic Resonance Images (DW-MRI). Different physiological-based modeling approaches of the diffusion process have been submitted to validation by sequentially incorporating prior knowledge on the MCR constraints. Results validate the biexponential diffusion modeling approach and show the capability of the MCR models to find, characterize and locate the behaviors related to the presence of an early prostate tumor.The authors want to thank prof. Anna de Juan for her comments and help in using the software for this study. This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI 2011-28112-004-02.Aguado Sarrió, E.; Prats-Montalbán, JM.; Sanz Requena, R.; Marti Bonmati, L.; Alberich Bayarri, Á.; Ferrer Riquelme, AJ. (2015). Prostate Diffusion Weighted-Magnetic Resonance Image analysis using Multivariate Curve Resolution methods. Chemometrics and Intelligent Laboratory Systems. 140:43-48. https://doi.org/10.1016/j.chemolab.2014.11.002S434814

Partial Least Squares - Diffusion Tensor Imaging (PLS-DTI): A novel approach for biomarker imaging in breast cancer

[EN] Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancer processes in early stages. Regarding breast cancer, due to the characteristics of the tissue as it is formed by ducts (tubular structure), anisotropic diffusion should be considered instead of general isotropic Diffusion Weighted Imaging (DWI). Anisotropic diffusion is studied by applying a technique called Diffusion Tensor Imaging (DTI), where the diffusion gradient is applied with several different directions, calculated by Ordinary Least Squares (OLS) in clinical practice. In this paper, we propose a new DTI calculation method based on Partial Least Squares (PLS), which has some advantages over the traditional OLS calculation: i) the PLS model provides valid biomarkers (non-negative eigenvalues) in a larger percentage of pixels, improving the traditional OLS calculation and reducing the effect of noisier images; ii) OLS tensors are calculated pixel-by-pixel, whereas the PLS method calculates only one model taking advantage of the correlation structure between pixels with similar characteristics, obtaining more reliable estimations; iii) PLS performance is quite reliable when lowering the number of directions of the magnetic field, while this is not the case of OLS. PLS keeps providing a good solution even with low functional resolution equipment, reducing costs and acquisition times, which is an important advantage for its widespread use in value-based medicine-oriented clinical practice.This research was supported by the Spanish Government (Science and Innovation Ministry) under the project PID2020-119262RB-I00.Aguado-Sarrió, E.; Prats-Montalbán, JM.; Robles-Lozano, G.; Camps-Herrero, J.; Ferrer, A. (2023). Partial Least Squares - Diffusion Tensor Imaging (PLS-DTI): A novel approach for biomarker imaging in breast cancer. Chemometrics and Intelligent Laboratory Systems. 235:1-11. https://doi.org/10.1016/j.chemolab.2023.10477711123

Sequential multiblock partial least squares discriminant analysis for assessing prostate cancer aggressiveness with multiparametric magnetic resonance imaging

[EN] In current radiology practice, multi-parametric magnetic resonance imaging (mpMRI) has recently become a key tool in diagnostic and therapeutic decisions. Although it is based on the subjective assessment of T2-weighted images, as well as perfusion-weighted and diffusion-weighted sequences, further quantitative parameters can also be derived from them for improving lesion phenotyping. Despite these parameters are usually exploited in a univariate way, ignoring the benefits of a real multivariate approach, still it is the gold standard imaging technique to assess prostate cancer location and probability of malignancy. In this paper, pharmacokinetic (perfusion) and exponential (diffusion) clinical models, as well as latent variable-based multivariate statistical models like multivariate curve resolution-alternating least squares (MCR-ALS), have been calculated and analyzed with sequential multi block-partial least squares discriminant analysis (SMB-PLS-DA) including technique-block differentiation, in order to better assess for cancer aggressiveness based on Gleason scales. The best prediction result was achieved by the ordered combination of diffusion blocks (MCR-ALS and exponential models) and normalized T2 values. The perfusion blocks did not improve the results obtained by diffusion and T2-weighted based parameters alone, so they can be removed from the SMB-PLS-DA model.Acknowledgements This research was partially supported by the Spanish Government (Science and Innovation Ministry) under the project PID2020-119262RB-I00, and by the Generalitat Valenciana under the project AICO/2021/111.Aguado-Sarrió, E.; Prats-Montalbán, JM.; Sanz-Requena, R.; Duchesne, C.; Ferrer, A. (2022). Sequential multiblock partial least squares discriminant analysis for assessing prostate cancer aggressiveness with multiparametric magnetic resonance imaging. Chemometrics and Intelligent Laboratory Systems. 226:1-13. https://doi.org/10.1016/j.chemolab.2022.10458811322