A generative approach for image-based modeling of tumor growth

22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. ProceedingsExtensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10)Academy of Finland (133611)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41- RR13218)National Institutes of Health (U.S.) (NINDS R01-NS051826)National Institutes of Health (U.S.) (NIH R01-NS052585)National Institutes of Health (U.S.) (NIH R01-EB006758)National Institutes of Health (U.S.) (NIH R01-EB009051)National Institutes of Health (U.S.) (NIH P41-RR014075)National Science Foundation (U.S.) (CAREER Award 0642971

From tumour perfusion to drug delivery and clinical translation of in silico cancer models

In silico cancer models have demonstrated great potential as a tool to improve drug design, optimise the delivery of drugs to target sites in the host tissue and, hence, improve therapeutic efficacy and patient outcome. However, there are significant barriers to the successful translation of in silico technology from bench to bedside. More precisely, the specification of unknown model parameters, the necessity for models to adequately reflect in vivo conditions, and the limited amount of pertinent validation data to evaluate models' accuracy and assess their reliability, pose major obstacles in the path towards their clinical translation. This review aims to capture the state-of-the-art in in silico cancer modelling of vascularised solid tumour growth, and identify the important advances and barriers to success of these models in clinical oncology. Particular emphasis has been put on continuum-based models of cancer since they - amongst the class of mechanistic spatio-temporal modelling approaches - are well-established in simulating transport phenomena and the biomechanics of tissues, and have demonstrated potential for clinical translation. Three important avenues in in silico modelling are considered in this contribution: first, since systemic therapy is a major cancer treatment approach, we start with an overview of the tumour perfusion and angiogenesis in silico models. Next, we present the state-of-the-art in silico work encompassing the delivery of chemotherapeutic agents to cancer nanomedicines through the bloodstream, and then review continuum-based modelling approaches that demonstrate great promise for successful clinical translation. We conclude with a discussion of what we view to be the key challenges and opportunities for in silico modelling in personalised and precision medicine

Image Registration for Quantitative Parametric Response Mapping of Cancer Treatment Response

AbstractImaging biomarkers capable of early quantification of tumor response to therapy would provide an opportunity to individualize patient care. Image registration of longitudinal scans provides a method of detecting treatment-associated changes within heterogeneous tumors by monitoring alterations in the quantitative value of individual voxels over time, which is unattainable by traditional volumetric-based histogram methods. The concepts involved in the use of image registration for tracking and quantifying breast cancer treatment response using parametric response mapping (PRM), a voxel-based analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) scans, are presented. Application of PRM to breast tumor response detection is described, wherein robust registration solutions for tracking small changes in water diffusivity in breast tumors during therapy are required. Methodologies that employ simulations are presented for measuring expected statistical accuracy of PRM for response assessment. Test-retest clinical scans are used to yield estimates of system noise to indicate significant changes in voxel-based changes in water diffusivity. Overall, registration-based PRM image analysis provides significant opportunities for voxel-based image analysis to provide the required accuracy for early assessment of response to treatment in breast cancer patients receiving neoadjuvant chemotherapy

The nucleus under the microscope. A biophysical approach.

At the beginning of 1950, many researchers challenged the possibility to overcome the fundamental Abbe limit. An attempt was made by Giuliano Toraldo di Francia, who showed that the width of the point-spread function can be reduced applying a filtering technique (called apodozation) (1). In 1994, a revolutionary event took place in the field of optical microscopy: Hell described a method for circumventing the light diffraction barrier (2). In this way, details that were not visible in diffraction-limited techniques could be imaged using a fluorescence microscopy. Nowadays, these methods termed Far-field fluorescence microscopy or nanoscopy techniques, has become an indispensable tool for scientist to address important biological and biophysical questions at the single molecule level. To highlight the outstanding importance of such techniques, the Royal Swedish Academy of Sciences awarded Eric Betzig, Stefan W. Hell, and William E. Moerner the Nobel Prize in Chemistry 2014 \u201cfor the development of super-resolved fluorescence microscopy\u201d. In addition, several important technical improvements, including confocal laser scanning microscopy (CLSM) (3), multiphoton microscopy, 4Pi (4) and I5M (5) have had an important role in the field of optical microscopy. On the other side, in 2015 Boyden and colleagues developed a new method termed Expansion Microscopy (ExM), which allows expanding uniformly biological samples by increasing the relative distances among fluorescent molecules labelling specific cellular components (6). ExM permits to achieve a lateral resolution of about 65 nm, using a conventional - diffracted microscope. However, all super resolution methods demand a particular attention in the sample preparation. Achieving super resolved images require the optimization of every steps involved in the labelling process, from the expression of a fluorescent proteins to the fixation of the biological samples. In the last years, these labelling strategies have obtained a critical role in the field of fluorescence microscopy. In particular, the design and the localization precision of specific affinity probes are crucial features that can restrict the applicability of these techniques. In this work, several labelling approaches and optimization of different staining protocol for super resolution techniques were addressed. My effort was focused on STED nanoscopy and ExM, and how to optimize the labelling protocol, the fluorophores choice for a high labelling density. The optimization of the steps involved in the labelling processes allows me combining ExM with STED nanoscopy (ExSTED), to enhance the final resolution (7). In addition, these techniques were used to decipher molecular assemblies in the cellular nuclei. In particular, my attention was focused on an important layer termed nuclear envelope (NE) (8). This nuclear region encases the genetic material, maintains the regular shape of the nucleus and regulates the gene expression. NE is composed by two lipid bilayer and different class of proteins, which pass through or are strictly linked to the nuclear membranes. Nuclear pore complexes (NPCs) and nuclear lamins, two classes of proteins belonging to the NE, were investigated in this work. In particular, NPCs was used to evaluate the isotropy and calculate the expansion factor (EF) at the nanoscale level in ExM. In this work, we show that Nup153, a filamentous subunit localized in the nuclear pore basket (9), is a good reporter to verify the isotropy of the expansion process and its quantification. In addition, nuclear lamins, in particular lamin A (LA) and its mutation \u394LA50 (10), were used to investigate the physiological and pathological nuclear membrane invagination in normal and aging cells

Correlated Multimodal Imaging in Life Sciences:Expanding the Biomedical Horizon

International audienceThe frontiers of bioimaging are currently being pushed toward the integration and correlation of several modalities to tackle biomedical research questions holistically and across multiple scales. Correlated Multimodal Imaging (CMI) gathers information about exactly the same specimen with two or more complementary modalities that-in combination-create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows to describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, diseases or organisms by untangling their molecular mechanisms within their native environment. The two best-established CMI implementations for small animals and model organisms are hardware-fused platforms in preclinical imaging (Hybrid Imaging) and Correlated Light and Electron Microscopy (CLEM) in biological imaging. Although the merits of Preclinical Hybrid Imaging (PHI) and CLEM are well-established, both approaches would benefit from standardization of protocols, ontologies and data handling, and the development of optimized and advanced implementations. Specifically, CMI pipelines that aim at bridging preclinical and biological imaging beyond CLEM and PHI are rare but bear great potential to substantially advance both bioimaging and biomedical research. CMI faces three mai