Tumor classification and prediction using robust multivariate clustering of multiparametric MRI

International audienceIn neuro-oncology, the use of multiparametric MRI may better characterize brain tumor heterogeneity. To fully exploit multiparametric MRI (e.g. tumor classification), appropriate analysis methods are yet to be developed. In this work, we show on small animals data that advanced statistical learning approaches can help 1) in organizing existing data by detecting and excluding outliers and 2) in building a dictionary of tumor fingerprints from a clustering analysis of their microvascular features

Monitoring glioma heterogeneity during tumor growth using clustering analysis of multiparametric MRI data

International audienceSynopsis Brain tumor heterogeneity plays a major role during gliomas growth and for the tumors resistance to therapies. The goal of this study was to demonstrate the ability of clustering analysis applied to multiparametric MRI (mpMRI) data to summarize and quantify intralesional heterogeneity during tumor growth. A mpMRI dataset of rats bearing glioma was acquired during the tumor growth (5 maps, 8 animals and 6 time points). After co-registration of every MR data over time, a clustering analysis was performed using a Gaussian mixture distribution model. Although preliminary, our results show that clustering analysis of mpMRI has a great potential to monitor quantitatively intralesional heterogeneity during the growth of tumors. Introduction For tumor diagnosis, histology often remains the reference, but due to tumor heterogeneity, it is widely acknowledged that biopsies are not reliable. There is thus a strong interest in monitoring quantitatively intralesional brain tumor heterogeneity. MRI has demonstrated its ability to quantitatively map structural information like diiusion (ADC) as well as functional characteristics such as the blood volume (BVf), vessel size (VSI), the oxygen saturation of the tissue (StO), or the blood brain barrier permeability. In a recent study (1), these MR parameters were analyzed independently from each other to demonstrate the great potential of a multiparametric MR (mpMRI) protocol to monitor combined radio-and chemo-therapies. However, to summarize and quantify all the information contained in an mpMRI protocol while preserving information about tumor heterogeneity, new methods to extract information need to be developed. The goal of this study is to demonstrate the ability of clustering analysis (2) applied to longitudinal mpMRI to summarize and quantify intralesional heterogeneity during tumor growth. Methods Animal model: The local IRB committee approved all studies. 9L tumors were implanted in 8 rats and imaging was performed every 2 days between day 7 and day 17 post tumor implantation on a 4.7T Bruker system (D7, D9, D11, D13, D15 and D17; respectively). The following mpMRI protocol was acquired at each MR session: a T2-weighted spin echo sequence to obtain structural information over the whole brain, a diiusion weighted EPI sequence to map the Apparent Diiusion Coeecient (ADC) and multiple spin/gradient echo sequences to map T2 and T2*. A Gradient Echo Sampling of the FID and Spin Echo (GESFIDE) sequence was acquired pre-and post-injection of USPIO (133 µmol/kg). A dynamic contrast enhancing sequence was acquired using a RARE sequence (T1w images; n=15, 15.6 sec per image). After the acquisition of 4 images, a bolus of gadolinium-chelate was administered (100µmol/kg). Parametric maps: for each MR session, BVf and VSI maps were computed using the approach described in (3), StO using the method described in (4) and the vessel permeability maps (Perm) was calculated as the percentage of enhancement (voxel-wise) within 3 min post injection of gadolinium (cf. g1-a). Co-registration: each parametric map of each MR session was co-registered to that acquired at the previous time point using rigid registration (SPM toolbox and Matlab). ROI: tumor was manually delineated using the T2w images (Tumor-ROI; Red line in g1-a). Cluster analysis: parameter values were centered and normalized. Then, a Gaussian mixture distribution (Matlab function called: tgmdist) was use to performed the clustering analysis of all voxels included in the tumor-ROI. The number of classes inside the mixture was selected by minimizing the Bayesian information criterion (BIC). Results Firstly, we performed the clustering analysis 9 times using 1 to 9 classes. The optimal classes number, deened by the BIC was 5. Each cluster may be seen as a tissue type, as described Fig.1-E. The result of the clustering analysis is illustrated Fig1-A for one animal. For each of the ve clusters (labeled K1 to K5), the evolution of the mean cluster volume over the entire population of tumor is presented Fig 1-B. Note that the sum of the ve cluster volumes represents the whole tumor volume. Fig.1-C illustrates the longitudinal evolution of the 5 clusters in 2 animals with diierent tumor growth rate (slow on the top and high on the bottom). Although the cluster analysis analyzed every voxel independently from each other, one can see that the clustering results are spatially consistent at 1 time point but also over time. Indeed, clusters are spatially grouped: for example, the green cluster is mostly located in the center of the tumor (Fig1-C). Our result shows a diierence in cluster composition between the slow and the high growth rate tumors (Fig.1-C,D). For example, in the slow growth rate tumor, the yellow cluster takes more and more space in the tumor overtime (up to 49% at D17) whereas, in the high growth rate tumor, it is the green one. The main diierence between the yellow and the green cluster is the strong reduction in StO in the green cluster versus the yellow cluster (cf. Fig.1-E). Conclusions To our knowledge, it is a rst study demonstrating the feasibility of performing a clustering analysis on mpMRI data to monitor the evolution of brain tumor heterogeneity in vivo. This approach highlights the type of tissue, which mostly contributes to the development of the tumor. The composition in tissue type could be used to reene the evaluation of chemo and radiotherapies and could contribute to improve tumor prognosis

Optimizing signal patterns for MR vascular fingerprinting

International audienceMR vascular fingerprinting proposes to map vascular properties such as blood volume fraction, average vessel radius or blood oxygenation saturation (SO). The fingerprint pattern used in previous studies provides low sensitivity on SO. We optimised signal patterns built from pre and post USPIO acquisitions. Concatenation of different echoes associated with higher dimensional dictionaries led to better estimates in both healthy and tumoral tissues

High-Precision Radiosurgical Dose Delivery by Interlaced Microbeam Arrays of High-Flux Low-Energy Synchrotron X-Rays

Microbeam Radiation Therapy (MRT) is a preclinical form of radiosurgery dedicated to brain tumor treatment. It uses micrometer-wide synchrotron-generated X-ray beams on the basis of spatial beam fractionation. Due to the radioresistance of normal brain vasculature to MRT, a continuous blood supply can be maintained which would in part explain the surprising tolerance of normal tissues to very high radiation doses (hundreds of Gy). Based on this well described normal tissue sparing effect of microplanar beams, we developed a new irradiation geometry which allows the delivery of a high uniform dose deposition at a given brain target whereas surrounding normal tissues are irradiated by well tolerated parallel microbeams only. Normal rat brains were exposed to 4 focally interlaced arrays of 10 microplanar beams (52 µm wide, spaced 200 µm on-center, 50 to 350 keV in energy range), targeted from 4 different ports, with a peak entrance dose of 200Gy each, to deliver an homogenous dose to a target volume of 7 mm3 in the caudate nucleus. Magnetic resonance imaging follow-up of rats showed a highly localized increase in blood vessel permeability, starting 1 week after irradiation. Contrast agent diffusion was confined to the target volume and was still observed 1 month after irradiation, along with histopathological changes, including damaged blood vessels. No changes in vessel permeability were detected in the normal brain tissue surrounding the target. The interlacing radiation-induced reduction of spontaneous seizures of epileptic rats illustrated the potential pre-clinical applications of this new irradiation geometry. Finally, Monte Carlo simulations performed on a human-sized head phantom suggested that synchrotron photons can be used for human radiosurgical applications. Our data show that interlaced microbeam irradiation allows a high homogeneous dose deposition in a brain target and leads to a confined tissue necrosis while sparing surrounding tissues. The use of synchrotron-generated X-rays enables delivery of high doses for destruction of small focal regions in human brains, with sharper dose fall-offs than those described in any other conventional radiation therapy

AGuIX® from bench to bedside-Transfer of an ultrasmall theranostic gadolinium-based nanoparticle to clinical medicine

International audienceAGuIX® are sub-5 nm nanoparticles made of a polysiloxane matrix and gadolinium chelates. This nanoparticle has been recently accepted in clinical trials in association with radiotherapy. This review will summarize the principal preclinical results that have led to first in man administration. No evidence of toxicity has been observed during regulatory toxicity tests on two animal species (rodents and monkeys). Biodistributions on different animal models have shown passive uptake in tumours due to enhanced permeability and retention effect combined with renal elimination of the nanoparticles after intravenous administration. High radiosensitizing effect has been observed with different types of irradiations in vitro and in vivo on a large number of cancer types (brain, lung, melanoma, head and neck…). The review concludes with the second generation of AGuIX nanoparticles and the first preliminary results on human

Carbone des sols en Afrique

Les sols sont une ressource essentielle à préserver pour la production d’aliments, de fibres, de biomasse, pour la filtration de l’eau, la préservation de la biodiversité et le stockage du carbone. En tant que réservoirs de carbone, les sols sont par ailleurs appelés à jouer un rôle primordial dans la lutte contre l’augmentation de la concentration de gaz à effet de serre. Ils sont ainsi au centre des objectifs de développement durable (ODD) des Nations unies, notamment les ODD 2 « Faim zéro », 13 « Lutte contre le changement climatique », 15 « Vie terrestre », 12 « Consommation et production responsables » ou encore 1 « Pas de pauvreté ». Cet ouvrage présente un état des lieux des sols africains dans toute leur diversité, mais au-delà, il documente les capacités de stockage de carbone selon les types de sols et leurs usages en Afrique. Il propose également des recommandations autour de l’acquisition et de l’interprétation des données, ainsi que des options pour préserver, voire augmenter les stocks de carbone dans les sols. Tous les chercheurs et acteurs du développement impliqués dans les recherches sur le rôle du carbone des sols sont concernés par cette synthèse collective. Fruit d’une collaboration entre chercheurs africains et européens, ce livre insiste sur la nécessité de prendre en compte la grande variété des contextes agricoles et forestiers africains pour améliorer nos connaissances sur les capacités de stockage de carbone des sols et lutter contre le changement climatique

Euclid Near Infrared Spectrometer and Photometer instrument concept and first test results obtained for different breadboards models at the end of phase C

The Euclid mission objective is to understand why the expansion of the Universe is accelerating through by mapping the geometry of the dark Universe by investigating the distance-redshift relationship and tracing the evolution of cosmic structures. The Euclid project is part of ESA's Cosmic Vision program with its launch planned for 2020 (ref [1]). The NISP (Near Infrared Spectrometer and Photometer) is one of the two Euclid instruments and is operating in the near-IR spectral region (900- 2000nm) as a photometer and spectrometer. The instrument is composed of: - a cold (135K) optomechanical subsystem consisting of a Silicon carbide structure, an optical assembly (corrector and camera lens), a filter wheel mechanism, a grism wheel mechanism, a calibration unit and a thermal control system - a detection subsystem based on a mosaic of 16 HAWAII2RG cooled to 95K with their front-end readout electronic cooled to 140K, integrated on a mechanical focal plane structure made with molybdenum and aluminum. The detection subsystem is mounted on the optomechanical subsystem structure - a warm electronic subsystem (280K) composed of a data processing / detector control unit and of an instrument control unit that interfaces with the spacecraft via a 1553 bus for command and control and via Spacewire links for science data This presentation describes the architecture of the instrument at the end of the phase C (Detailed Design Review), the expected performance, the technological key challenges and preliminary test results obtained for different NISP subsystem breadboards and for the NISP Structural and Thermal model (STM)

Cluster versus ROI analysis to assess combined antiangiogenic therapy and radiotherapy in the F98 rat-glioma model

International audienceFor glioblastoma (GBM), current therapeutic approaches focus on the combination of several therapies, each of them individually approved for GBM or other tumor types. Many efforts are made to decipher the best sequence of treatments that would ultimately promote the most efficient tumor response. There is therefore a strong interest in developing new clinical in vivo imaging procedures that can rapidly detect treatment efficacy and allow individual modulation of the treatment. In this preclinical study, we propose to evaluate tumor tissue changes under combined therapies, tumor vascular normalization under antiangiogenic treatment followed by radiotherapy, using a voxel-based clustering approach. This approach was applied to a rat model of glioma (F98). Six MRI parameters were mapped apparent diffusion coefficient, vessel wall permeability, cerebral blood volume fraction, cerebral blood flow, tissue oxygen saturation and vessel size index. We compared the classical region of interest (ROI)-based analysis with a cluster-based analysis. Five clusters, defined by their MRI features, were sufficient to characterize tumor progression and tumor changes during treatments. These results suggest that the cluster-based analysis was as efficient as the ROI-based analysis to assess tumor physiological changes during treatment, but also gave additional information regarding the voxels impacted by treatments and their localization within the tumor. Overall, cluster-based analysis appears to be a powerful tool for subtle monitoring of tumor changes during combined therapies

Monitoring glioma heterogeneity during tumor growth using clustering analysis of multiparametric MRI data

International audienceSynopsis Brain tumor heterogeneity plays a major role during gliomas growth and for the tumors resistance to therapies. The goal of this study was to demonstrate the ability of clustering analysis applied to multiparametric MRI (mpMRI) data to summarize and quantify intralesional heterogeneity during tumor growth. A mpMRI dataset of rats bearing glioma was acquired during the tumor growth (5 maps, 8 animals and 6 time points). After co-registration of every MR data over time, a clustering analysis was performed using a Gaussian mixture distribution model. Although preliminary, our results show that clustering analysis of mpMRI has a great potential to monitor quantitatively intralesional heterogeneity during the growth of tumors. Introduction For tumor diagnosis, histology often remains the reference, but due to tumor heterogeneity, it is widely acknowledged that biopsies are not reliable. There is thus a strong interest in monitoring quantitatively intralesional brain tumor heterogeneity. MRI has demonstrated its ability to quantitatively map structural information like diiusion (ADC) as well as functional characteristics such as the blood volume (BVf), vessel size (VSI), the oxygen saturation of the tissue (StO), or the blood brain barrier permeability. In a recent study (1), these MR parameters were analyzed independently from each other to demonstrate the great potential of a multiparametric MR (mpMRI) protocol to monitor combined radio-and chemo-therapies. However, to summarize and quantify all the information contained in an mpMRI protocol while preserving information about tumor heterogeneity, new methods to extract information need to be developed. The goal of this study is to demonstrate the ability of clustering analysis (2) applied to longitudinal mpMRI to summarize and quantify intralesional heterogeneity during tumor growth. Methods Animal model: The local IRB committee approved all studies. 9L tumors were implanted in 8 rats and imaging was performed every 2 days between day 7 and day 17 post tumor implantation on a 4.7T Bruker system (D7, D9, D11, D13, D15 and D17; respectively). The following mpMRI protocol was acquired at each MR session: a T2-weighted spin echo sequence to obtain structural information over the whole brain, a diiusion weighted EPI sequence to map the Apparent Diiusion Coeecient (ADC) and multiple spin/gradient echo sequences to map T2 and T2*. A Gradient Echo Sampling of the FID and Spin Echo (GESFIDE) sequence was acquired pre-and post-injection of USPIO (133 µmol/kg). A dynamic contrast enhancing sequence was acquired using a RARE sequence (T1w images; n=15, 15.6 sec per image). After the acquisition of 4 images, a bolus of gadolinium-chelate was administered (100µmol/kg). Parametric maps: for each MR session, BVf and VSI maps were computed using the approach described in (3), StO using the method described in (4) and the vessel permeability maps (Perm) was calculated as the percentage of enhancement (voxel-wise) within 3 min post injection of gadolinium (cf. g1-a). Co-registration: each parametric map of each MR session was co-registered to that acquired at the previous time point using rigid registration (SPM toolbox and Matlab). ROI: tumor was manually delineated using the T2w images (Tumor-ROI; Red line in g1-a). Cluster analysis: parameter values were centered and normalized. Then, a Gaussian mixture distribution (Matlab function called: tgmdist) was use to performed the clustering analysis of all voxels included in the tumor-ROI. The number of classes inside the mixture was selected by minimizing the Bayesian information criterion (BIC). Results Firstly, we performed the clustering analysis 9 times using 1 to 9 classes. The optimal classes number, deened by the BIC was 5. Each cluster may be seen as a tissue type, as described Fig.1-E. The result of the clustering analysis is illustrated Fig1-A for one animal. For each of the ve clusters (labeled K1 to K5), the evolution of the mean cluster volume over the entire population of tumor is presented Fig 1-B. Note that the sum of the ve cluster volumes represents the whole tumor volume. Fig.1-C illustrates the longitudinal evolution of the 5 clusters in 2 animals with diierent tumor growth rate (slow on the top and high on the bottom). Although the cluster analysis analyzed every voxel independently from each other, one can see that the clustering results are spatially consistent at 1 time point but also over time. Indeed, clusters are spatially grouped: for example, the green cluster is mostly located in the center of the tumor (Fig1-C). Our result shows a diierence in cluster composition between the slow and the high growth rate tumors (Fig.1-C,D). For example, in the slow growth rate tumor, the yellow cluster takes more and more space in the tumor overtime (up to 49% at D17) whereas, in the high growth rate tumor, it is the green one. The main diierence between the yellow and the green cluster is the strong reduction in StO in the green cluster versus the yellow cluster (cf. Fig.1-E). Conclusions To our knowledge, it is a rst study demonstrating the feasibility of performing a clustering analysis on mpMRI data to monitor the evolution of brain tumor heterogeneity in vivo. This approach highlights the type of tissue, which mostly contributes to the development of the tumor. The composition in tissue type could be used to reene the evaluation of chemo and radiotherapies and could contribute to improve tumor prognosis