C.A. Cuenod

Radiological evaluation of response to treatment: Application to metastatic renal cancers receiving anti-angiogenic treatment

Targeted therapies have considerably improved the prognosis of patients with metastatic renal cancer (mRCC) but there are no reliable response assessment criteria reflecting the clinical benefits, because there is no regression in size, or it is delayed. Such criteria would help early identification of non-responders, who would then benefit from a change of treatment, and would avoid their being subjected to unnecessary side effects related to the treatment. We will review the imaging techniques currently available for evaluating tumour response in mRCC patients, including the response evaluation criteria in solid tumours (RECIST), the Choi criteria, the modified Choi criteria, and the CT size and attenuation criteria (SACT). We will also discuss functional imaging techniques, which are based on the physiological characteristics of the tumours, such as perfusion CT, magnetic resonance imaging or ultrasound (DCE-CT, DCE-MRI, DCE-US), diffusion MRI, BOLD MRI and new positron emission tomography (PET) tracers. It is not possible at present to propose a unanimously acknowledged criterion for evaluating tumour response to targeted therapy. However, there is a real need for this according to oncologists and the pharmaceutical industry, and radiologists need to be involved in reflecting on the subject.

A SAEM algorithm for the estimation of template and deformation parameters in medical image sequences

This paper is about object deformations observed throughout a sequence of images. We present a statistical framework in which the observed images are defined as noisy realizations of a randomly deformed template image. In this framework, we focus on the problem of the estimation of parameters related to the template and deformations. Our main motivation is the construction of estimation framework and algorithm which can be applied to short sequences of complex and highly-dimensional images. The originality of our approach lies in the representations of the template and deformations, which are defined on a common triangulated domain, adapted to the geometry of the observed images. In this way, we have joint representations of the template and deformations which are compact and parsimonious. Using such representations, we are able to drastically reduce the number of parameters in the model. Besides, we adapt to our framework the Stochastic Approximation EM algorithm combined with a Markov Chain Monte Carlo procedure which was proposed in 2004 by Kuhn and Lavielle. Our implementation of this algorithm takes advantage of some properties which are specific to our framework. More precisely, we use the Markovian properties of deformations to build an efficient simulation strategy based on a Metropolis-Hasting-Within-Gibbs sampler. Finally, we present some experiments on sequences of medical images and synthetic data.

A classifying registration technique for the estimation of enhancement curves of DCE-CT scan sequences

In this paper, we propose a new technique for the estimation of contrast enhancement curves of Dynamic Contrast-Enhanced sequences, which takes the most from the interdependence between this estimation problem and the registration problem raised by possible movements occurring in sequences. The technique solves the estimation and registration problems simultaneously in an iterative way. However, unlike previous techniques, a pixel classification scheme is included within the estimation so as to compute enhancement curves on pixel classes instead of single pixels. The classification scheme is designed using a descendant hierarchical approach. Due to this tree approach, the number of classes is set automatically and the whole technique is entirely unsupervised. Moreover, some specific prior information about the shape of enhancement curves are included in the splitting and pruning steps of the classification scheme. Such an information ensures that created classes include pixels having homogeneous and relevant enhancement properties. The technique is applied to DET-CT scan sequences and evaluated using ground truth data. Results show that classifications are anatomically sound and that contrast enhancements are accurately estimated from sequences.

Lipidic spherulites as magnetic resonance imaging contrast agents

Magnetic resonance imaging is an excellent technique to achieve anatomical details and highly resolved images. The search for efficient contrast agents to increase the signal to background ratio led us to evaluate paramagnetic spherulites as potential Magnetic Resonance Imaging (MRI) contrast agents. Spherulites are supramolecular assemblies, made of lipidic concentric multilayers, able to encapsulate with high efficiency soluble macromolecules. Despite their highly interesting structure, spherulites have never been proposed as imaging agents. We proposed here three approaches to render spherulites paramagnetic: encapsulating a soluble contrastophore, inserting a lipidic contrastophore derivative or grafting a soluble contrastophore at the surface of the spherulites. Following similar strategies, liposomes were prepared for comparison. The conservation of the spherulite structure, throughout these three strategies, was shown by cryoelectron microscopy and small angle light scattering. The effect of the paramagnetic spherulites was studied by magnetic resonance imaging at different magnetic fields. The results showed that insertion of a contrastophore lipidic derivative into spherulite bilayers and grafting a contrastophore at the surface of the spherulites were the two strategies which led to the highest MRI contrast improvement.

MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES

We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. In this approach we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way that selects factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.

Biomarqueurs en imagerie pour l’évaluation des nouvelles thérapies anticancéreuses

Evaluation of treatments is becoming a critical issue in oncology for the development of new drogues and for individual treatment monitoring. The criteria commonly used for these evaluations are based on the evolution of the size of the lesions; The RECIST criteria being the more commonly used. Tumor shrinking, however, is a late event and is moderate with the new targeted drugs such as the antiangiogenic molecules. Therefore, the evaluation of new drugs requires large cohorts and the individual monitoring is not optimal. The use of functional imaging techniques such as MRI, CT, US or positron emission tomography (PET) is shading new hopes in the field, because the functional parameters given by these new techniques (perfusion, permeability, diffusion, metabolism, chemical composition or elasticity) are altered early under therapy. These functional parameters may become useful biomarkers for the development of the new drugs and for individual monitoring. They need, however, to be evaluated on larger studies and to be standardized.RésuméL’évaluation des traitements est un enjeu de plus en plus important en cancérologie, qu’il s’agisse de l’évaluation des nouveaux traitements ou du monitoring individuel d’un patient. Les critères utilisés actuellement sont morphologiques, basés sur l’évolution de la tailledeslésions, telsque les critères RECIST. Mais les variations de tailles sont retardées, et avec les nouveaux traitements ciblés tels que les antiangiogéniques, elles sont faibles. Cela implique l’utilisation de larges cohortes pour mettre en évidence l’efficacité d’un traitement et rend difficile le monitoring individuel. L’utilisation des techniques d’imagerie fonctionnelle, en scanner, en IRM, en échographie ou en tomographie par émission de positrons (TEP), apporte de nouveaux espoirs. Les paramètres obtenus grâce à ces techniques(perfusion, perméabilité, diffusion, métabolisme, composition, élasticité) sont modifiés précocement par les traitements. Ces paramètres fonctionnels pourraient devenir des biomarqueurs pour l’évaluation et le suivi des traitements. Les études ayant démontré leur utilité sont préliminaires, il reste à standardiser les techniques d’acquisition et à évaluer à plus large échelle, en fonction des situations cliniques, quels sont les paramètres fonctionnels les plus adaptés.