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Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancerous processes in early stages. As for breast cancer, due to the tubular structure of the tissue, being formed by ducts, anisotropic diffusion should be considered instead of the general isotropic diffusion. Anisotropic diffusion is studied by applying a technique called Diffusion Tensor Imaging (DTI), where the diffusion gradient is applied by changing the magnetic field in several spatial directions. To date, the application of Multivariate Curve Resolution (MCR) models in diffusion sequences has demonstrated its ability to develop cancer biomarkers of easy clinical interpretation in the case of isotropic tissues, such as the prostate. But so far, it has never been applied in the case of anisotropic tissues, as the breast. Therefore, the main objective of this work is to obtain easy-to-interpret imaging biomarkers useful for early breast cancer diagnosis from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models. A classification model to identify healthy and tumor affected pixels is also proposed. • New MCR-based biomarkers calculated from the Diffusion Tensor are proposed to improve the identification of breast tumors. • PLS-DA, Random Forest, Logistic regression and SVM have been applied to discriminate between healthy and lesion tissues. • MCR biomarkers are statistically significant better than DTI biomarkers improving the results of classification parameters. [ABSTRACT FROM AUTHOR]

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. • A PLS-based approach is proposed for the calculation of the DTI with the objective of better assessing breast cancer. • The eigenvalues and the proposed biomarkers: dmax and 1-Rmax have been proved as helpful parameters for identifying tumors. • PLS outperforms OLS when the number of gradient directions is low (6). OLS cannot perform properly in these conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. • The capability of MRI clinical and MCR-ALS models have been jointly studied in order to better assess cancer aggressiveness. • SMB-PLS-DA has been employed for selecting the best sources in order to discriminate aggressiveness based on Gleason scales. • Statistical ​significant results shows that perfusion models do not provide any additional relevant information. [ABSTRACT FROM AUTHOR]