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Quantification of bone tissue heterogeneity and cell distributionpatterns from digital histology: application to osteosarcoma

Mancini, Anthony and Gomez-Brouchet, Anne and Quintard, Michel and Lorthois, Sylvie and Swider, Pascal and Assemat, Pauline Quantification of bone tissue heterogeneity and cell distributionpatterns from digital histology: application to osteosarcoma. (2020) In: QBI 2020 conference, 6 January 2020 - 9 January 2020 (Oxford, United Kingdom). (Unpublished)

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Like most sarcomas with complex genomics,or more generally bone tissues, osteosarcoma isa type of tumors exhibiting a strong spatialheterogeneity of the micro-environment. Thisheterogeneity makes the diagnostic complex andcan induce strong spatial variability in theresponse to treatments. New researchstrategies are consequently needed tounderstand the impact of spatial heterogeneity onthe diagnostic accuracy and on the treatmentefficiency, and more generally to understand thelinks between tissue scale bone matrix structuresand underlying biology occurring at the cell scale.The aim of this interdisciplinary work is to obtain the quantification of correlations between clinicaldata, heterogeneity of bone tissues and mechanobiological parameters. To this purpose, original numerical developments were initiated in our group to study the intratumoral and healthy bone tissue heterogeneity from histological and immunohistological sections. The code aimed at obtaining quantitative metrics of the cell population distribution, of the bone matrix micro-architecture (porosity) and of the transport properties (such as effective diffusivity). Because tissues exhibit naturally a complex spatial scales cascade, it can be modeled, at the tissue scale, as a three phases porous medium (fluid, solid, cell populations). Using methodologies related to porous media analysis, characteristic lengths were extracted and correlations of phenomena occurring cell and tissue scale examined. Further developments permitted the calculation of effective mechanical properties. The methodology used successive algorithms of machine learning for the histological image segmentation and a combination of iterative algorithms and filters for the correlation calculations. Results put forward the strength of this approach for the identification of new markers in the study of pathological bone tissues.

Item Type:Conference or Workshop Item (Other)
HAL Id:hal-03113638
Audience (conference):International conference without published proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Other partners > Centre Hospitalier Universitaire de Toulouse - CHU Toulouse (FRANCE)
Laboratory name:
Deposited On:16 Dec 2020 13:52

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