Quantification of bone tissue heterogeneity and cell distributionpatterns from digital histology: application to osteosarcoma Article Swipe
Anthony Mancini
,
Anne Gomez‐Brouchet
,
Michel Quintard
,
Sylvie Lorthois
,
Pascal Swider
,
Pauline Assémat
·
YOU?
·
· 2020
· Open Access
·
YOU?
·
· 2020
· Open Access
·
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.
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Metadata
- Type
- preprint
- Language
- en
- https://oatao.univ-toulouse.fr/27133/1/27133_Mancini.pdf
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3111294978Canonical identifier for this work in OpenAlex
- Title
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Quantification of bone tissue heterogeneity and cell distributionpatterns from digital histology: application to osteosarcomaWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-01-01Full publication date if available
- Authors
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Anthony Mancini, Anne Gomez‐Brouchet, Michel Quintard, Sylvie Lorthois, Pascal Swider, Pauline AssématList of authors in order
- PDF URL
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https://oatao.univ-toulouse.fr/27133/1/27133_Mancini.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://oatao.univ-toulouse.fr/27133/1/27133_Mancini.pdfDirect OA link when available
- Concepts
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Computer science, Bone tissue, Osteosarcoma, Population, Biomedical engineering, Matrix (chemical analysis), Segmentation, Biological system, Artificial intelligence, Materials science, Pathology, Biology, Medicine, Environmental health, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| publication_date | 2020-01-01 |
| publication_year | 2020 |
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