Analyse différentielle de puces à ADN. Comparaison entre méthodes wrapper et filter.

13Dans le cadre de données d'expression génétique, nous nous intéressons aux méthodes qui permettent d'identifier les gènes significativement différentiellement exprimés entre deux situations biologiques. Nous allons comparer une méthode classique d'analyse par tests d'hypothèses à des méthodes d'analyse différentielle par régression régularisée. La difficulté de ce genre de jeu de données est la profusion de variables (les gènes) pour assez peu d'individus (les profils d'expression). La stratégie usuelle consiste à mettre en oeuvre autant de tests qu'il y a de variables et de considérer que les variables principales sont celles qui ont la « meilleure »p-value. Une stratégie alternative pourrait consister à choisir de classer les variables non plus en fonction de leur significativité (pour un test), mais plutôt de le classer suivant leur poids dans le modèle régularisé obtenu. Dans la bibliographie, les premières méthodes sont dites filter1, les deuxièmes sont plutôt dites wrapper2. Un bon aperçu de ce que sont les méthodes wrapper et filter est donné dans [9]. Le cadre ressemble à celui de l'apprentissage supervisé, car on dispose de profils d'expression géniques pour si possible l'ensemble du génome d'un organisme, chaque puce appartenant à une classe- situation biologique particulière (par exemple malade vs sain). L'implémentation des méthodes évoquées dans ce rapport a été effectuée sous R [16]

Evaluation of alkali-activated binders suitability for the stabilization/solidification of tunnel boring muds

In Europe, two major civil engineering projects, the “Grand Paris” automatic metro lines and the “Lyon-Turin” high speed railway tunnel, brought forth for the construction industry an unprecedented prerequisite within schemes of this scale: the necessity to recycle all forms of waste that comes out of the excavation process. By 2030, up to 100 million tons of muds retrieved from the boring operations, likely contaminated with sulphate and heavy metals, will have to be dealt with. Please click Additional Files below to see the full abstract

Comparison of different segmentation approaches without using gold standard. Application to the estimation of the left ventricle ejection fraction from cardiac cine MRI sequences.

International audienceA statistical method is proposed to compare several estimates of a relevant clinical parameter when no gold standard is available. The method is illustrated by considering the left ventricle ejection fraction derived from cardiac magnetic resonance images and computed using seven approaches with different degrees of automation. The proposed method did not use any a priori regarding with the reliability of each method and its degree of automation. The results showed that the most accurate estimates of the ejection fraction were obtained using manual segmentations, followed by the semiautomatic methods, while the methods with the least user input yielded the least accurate ejection fraction estimates. These results were consistent with the expected performance of the estimation methods, suggesting that the proposed statistical approach might be helpful to assess the performance of estimation methods on clinical data for which no gold standard is available

Nonsupervised Ranking of Different Segmentation Approaches: Application to the Estimation of the Left Ventricular Ejection Fraction From Cardiac Cine MRI Sequences

International audienceA statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was applied to rank eightmethods without using any a priori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the statistical distribution of the parameter of interest in the database. The ranking of the methods relies on figures of merit derived from the regression and computed using a bootstrap process. The methodology was applied to the estimation of the left ventricular ejection fraction derived from cardiac magnetic resonance images segmented using eight approaches with different degrees of automation: three segmentations were entirely manually performed and the others were variously automated. The ranking of methods was consistent with the expected performance of the estimation methods: the most accurate estimates of the ejection fraction were obtained using manual segmentations. The robustness of the ranking was demonstrated when at least three methods were compared. These results suggest that the proposed statistical approach might be helpful to assess the performance of estimation methods on clinical data for which no gold standard is available

Improved estimation of the left ventricular ejection fraction using a combination of independent automated segmentation results in cardiovascular magnetic resonance imaging

—This work aimed at combining different segmenta-tion approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by focusing on the left ventricular ejection fraction (LVEF) estimate resulting from the LV contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations, were studied, and sixteen combinations of the five automated methods were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates of the LVEF than individual automated segmentation methods. In addition, LVEF obtained with STAPLE were within inter-expert variability. Overall, combining different automated segmentation methods improved the reliability of the segmenta-tion result compared to that obtained using an individual metho

A mutual reference shape based on information theory

International audienceIn this paper, we propose to consider the estimation of a refer-ence shape from a set of different segmentation results using both active contours and information theory. The reference shape is defined as the minimum of a criterion that benefits from both the mutual information and the joint entropy of the input segmentations and called a mutual shape. This energy criterion is here justified using similarities between informa-tion theory quantities and area measures, and presented in a continuous variational framework. This framework brings out some interesting evaluation measures such as the speci-ficity and sensitivity. In order to solve this shape optimization problem, shape derivatives are computed for each term of the criterion and interpreted as an evolution equation of an active contour. Some synthetical examples allow us to cast the light on the difference between our mutual shape and an average shape. Our framework has been considered for the estimation of a mutual shape for the evaluation of cardiac segmentation methods in MRI

Consistency of aortic distensibility and pulse wave velocity estimates with respect to the Bramwell-Hill theoretical model: a cardiovascular magnetic resonance study

<p>Abstract</p> <p>Background</p> <p>Arterial stiffness is considered as an independent predictor of cardiovascular mortality, and is increasingly used in clinical practice. This study aimed at evaluating the consistency of the automated estimation of regional and local aortic stiffness indices from cardiovascular magnetic resonance (CMR) data.</p> <p>Results</p> <p>Forty-six healthy subjects underwent carotid-femoral pulse wave velocity measurements (<it>CF_PWV</it>) by applanation tonometry and CMR with steady-state free-precession and phase contrast acquisitions at the level of the aortic arch. These data were used for the automated evaluation of the aortic arch pulse wave velocity (<it>Arch_PWV</it>), and the ascending aorta distensibility (<it>AA_Distc, AA_Distb)</it>, which were estimated from ascending aorta strain (<it>AA_Strain</it>) combined with either carotid or brachial pulse pressure. The local ascending aorta pulse wave velocity <it>AA_PWVc </it>and <it>AA_PWVb </it>were estimated respectively from these carotid and brachial derived distensibility indices according to the Bramwell-Hill theoretical model, and were compared with the <it>Arch_PWV</it>. In addition, a reproducibility analysis of <it>AA_PWV </it>measurement and its comparison with the standard <it>CF_PWV </it>was performed. Characterization according to the Bramwell-Hill equation resulted in good correlations between <it>Arch_PWV </it>and both local distensibility indices <it>AA_Distc </it>(r = 0.71, p < 0.001) and <it>AA_Distb </it>(r = 0.60, p < 0.001); and between <it>Arch_PWV </it>and both theoretical local indices <it>AA_PWVc </it>(r = 0.78, p < 0.001) and <it>AA_PWVb </it>(r = 0.78, p < 0.001). Furthermore, the <it>Arch_PWV </it>was well related to <it>CF_PWV </it>(r = 0.69, p < 0.001) and its estimation was highly reproducible (inter-operator variability: 7.1%).</p> <p>Conclusions</p> <p>The present work confirmed the consistency and robustness of the regional index <it>Arch_PWV </it>and the local indices <it>AA_Distc and AA_Distb </it>according to the theoretical model, as well as to the well established measurement of <it>CF_PWV</it>, demonstrating the relevance of the regional and local CMR indices.</p

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA