MRI in multiple myeloma : a pictorial review of diagnostic and post-treatment findings

Magnetic resonance imaging (MRI) is increasingly being used in the diagnostic work-up of patients with multiple myeloma. Since 2014, MRI findings are included in the new diagnostic criteria proposed by the International Myeloma Working Group. Patients with smouldering myeloma presenting with more than one unequivocal focal lesion in the bone marrow on MRI are considered having symptomatic myeloma requiring treatment, regardless of the presence of lytic bone lesions. However, bone marrow evaluation with MRI offers more than only morphological information regarding the detection of focal lesions in patients with MM. The overall performance of MRI is enhanced by applying dynamic contrast-enhanced MRI and diffusion weighted imaging sequences, providing additional functional information on bone marrow vascularization and cellularity. This pictorial review provides an overview of the most important imaging findings in patients with monoclonal gammopathy of undetermined significance, smouldering myeloma and multiple myeloma, by performing a 'total' MRI investigation with implications for the diagnosis, staging and response assessment. Main message aEuro cent Conventional MRI diagnoses multiple myeloma by assessing the infiltration pattern. aEuro cent Dynamic contrast-enhanced MRI diagnoses multiple myeloma by assessing vascularization and perfusion. aEuro cent Diffusion weighted imaging evaluates bone marrow composition and cellularity in multiple myeloma. aEuro cent Combined morphological and functional MRI provides optimal bone marrow assessment for staging. aEuro cent Combined morphological and functional MRI is of considerable value in treatment follow-up

A new least-squares adaptation scheme for the affine combination of two adaptive filters

Adaptive combinations of adaptive filters are an efficient approach to alleviate the different tradeoffs to which adaptive filters are subject. rrhe basic idea is to mix the outputs of two adaptive filters with complementar~Tcapabilities, so that the combination is able to retain the best properties of each component. In previous works, we proposed to use a convex combination, applying weights.A(n) and 1-.A(n), with A(n) E (0,1), to the filter components, where the mixing parameter.A(n) was updated to minimize the overall square error using stochastic gradient descent rules. In this paper, we present a new adaptation scheme for.A(n) based on the solution to a least-squares (L8) problem, where the mixing parameter is allowed to lie outside range [0, 1]. Such affine combinations have recently been shown to provide additional gains. Unlike some previous proposals, the new L8 cOlnbination scheme does not require any explicit knowledge about the component filters. The ability of the L8 scheme to achieve optimal values ofthe mixing parameter is illustrated with several experiments in both stationary and tracking situations. Index Terms-.Adaptive filters, combination of filters

7 - LK norm adaptive transversal filters

A whole family of Lk norm adaptive transversal filters is introduced and analyzed, in the context of plant identification, under hypotheses that are validated by simulation results . The analysis allows to establish general convergence conditions and to compare the performance of the elements of the family from the point of view of their speed of convergence-degree of convergence (final residual error variance) compromise ; the results of these comparisons depend on the plant noise distribution characteristics . The deterministic optimization of the adaption step is also formulated and evaluated by means of simulation . Finally, open research lines in this area are indicated.Analyse complète de filtres transversaux adaptatifs avec norme LK pour l'identification des systèmes. Cet analyse permet d'établir les conditions générales de convergenc

K nearest neighbours with mutual information for simultaneous classification and missing data imputation

Missing data is a common drawback in many real-life pattern classification scenarios. One of the most popular solutions is missing data imputation by the K nearest neighbours (KNN) algorithm. In this article, we propose a novel KNN imputation procedure using a feature-weighted distance metric based on mutual information (MI). This method provides a missing data estimation aimed at solving the classification task, i.e., it provides an imputed dataset which is directed toward improving the classification performance. The MI-based distance metric is also used to implement an effective KNN classifier. Experimental results on both artificial and real classification datasets are provided to illustrate the efficiency and the robustness of the proposed algorithm. (C) 2009 Elsevier B.V. All rights reserved