Digitale Pathologie in der Immunonkologie - Aktuelle Chancen und Herausforderungen: Überblick zur Analyse von Immunzellinfiltraten mittels Whole Slide Imaging
Background: Immuno-oncology requires objective and standardized methods for measuring immune cell infiltrates for therapy selection and clinical trials. Methods: Current approaches in applying digital pathology in immuno-oncology and developments in computational image analysis were analyzed. Result...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | German |
| Published: |
22 October 2018
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| In: |
Der Pathologe
Year: 2018, Volume: 39, Issue: 6, Pages: 539-545 |
| ISSN: | 1432-1963 |
| DOI: | 10.1007/s00292-018-0540-9 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1007/s00292-018-0540-9
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| Author Notes: | N. Grabe, W. Roth, S. Foersch |
MARC
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| 520 | |a Background: Immuno-oncology requires objective and standardized methods for measuring immune cell infiltrates for therapy selection and clinical trials. Methods: Current approaches in applying digital pathology in immuno-oncology and developments in computational image analysis were analyzed. Results: Since 2008, digital pathology has had an ever increasing importance in immuno-oncology. It is currently the only technology allowing the systematic and cost-effective quantitative spatial immune-profiling of patients. The analysis of immunological biomarkers requires integrated staining and image analysis strategies from single- to multistain on slide stacks. Statistical limits of the hypothesis to be tested have to be taken into account. Digital image analysis opens a new technological role for pathology in immuno-oncology and thereby serves as a key technological driver. Conclusion: Digital pathology delivers objective and quantitative data on the tumor microenvironment. But currently, a fully automatic, high-throughput analytics capability is still missing. Deep learning is the remedy for this, as it improves image analysis with increasing data availability. This requires the creation of systematic data collections but will in the end deliver standardized and automatic immunological analyses. | ||
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| 650 | 4 | |a Maschinenlernen | |
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| 650 | 4 | |a Tumor microenvironment | |
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