Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion

IMPACT OF MULTICENTER HARMONIZATION ON THE DETECTION OF RADIONECROSIS IN MULTIMODAL MRI RADIOMICS STUDY

International audienceThis study presents the effect of multi-center harmonization on the selection and classification of radiomics features extracted from recurrent glioblastoma multiforme (GBM) multi-modal MR images to detect radionecrosis

Co-optimisation de l'engagement de production et de la capacité de stockage associée à une ferme photovoltaïque, prenant en compte le vieillissement de la batterie.

Afin de satisfaire à un engagement de production en trapèze, on adjoint une batterie à une centrale photovoltaïque. Cette étude vise à établir un dimensionnement optimal de cette batterie en considérant deux critères antagonistes : l'énergie délestée et l'usure du système de stockage. L'engagement optimal dépendant de la capacité de stockage disponible, une co-optimisation est mise en place afin de proposer le meilleur engagement quelle que soit la capacité, et non pas un engagement issu d'une loi de gestion simplifiée. Par ailleurs, l'influence du modèle d'endommagement de la batterie est explorée. Trois modèles de vieillissement en cyclage sont comparés et leur influence sur le dimensionnement optimal étudiée. Il apparait qu'un modèle de vieillissement extrêmement simple permet d'obtenir des dimensionnements cohérents et de corriger significativement le résultat qui aurait été obtenu en l'absence de toute prise en compte de l'endommagement

Detection of subclinical ultrasound enthesopathy and nail disease in patients at risk of psoriatic arthritis

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Stochastic optimization of an Electric Vehicle Fleet Charging with Uncertain Photovoltaic Production

International audienceSimultaneous development of photovoltaic generation and electric vehicles strengthens the solicitations on the electric power system. This paper investigates the possible synergy between these players to jointly improve the production predictability while ensuring a low carbon mobility. It stands for a step towards a quantification of its economic and environmental fallout. First a context is described for a PV-EV collaboration. Then this is gathered into an optimization problem. Grid commitment constraints, battery aging and mobility needs are here considered from the environmental point of view of equivalent primary energy. Finally, a resolution method is presented which achieve an time-efficient optimization of the power flow for each vehicle, based on upstream computed charging policies. It relies on a stochastic modeling of both vehicles availability and forecast error of the PV production. The resolution framework is the stochastic dynamic programming, coupled with on-line minimization so as to avoid the curse of dimensionality. The proposed resolution enables to compute optimal power flow for each vehicle, even among large fleets. The emphasis is here set on a versatile resolution method which could take over many detailed objective functions