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Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology
BACKGROUND: The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363837/ https://www.ncbi.nlm.nih.gov/pubmed/36813293 http://dx.doi.org/10.1002/cnr2.1796 |
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author | Huss, Ralf Raffler, Johannes Märkl, Bruno |
author_facet | Huss, Ralf Raffler, Johannes Märkl, Bruno |
author_sort | Huss, Ralf |
collection | PubMed |
description | BACKGROUND: The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re‐)activate the patient's immune system and direct it against the individual cancer in the most effective way. RECENT FINDINGS: Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune‐oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune‐cancer‐network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer‐assisted development and clinical validation of such digital biomarker. CONCLUSIONS: The successful implementation of AI‐supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into “precision pathology” delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a “precision oncology”. |
format | Online Article Text |
id | pubmed-10363837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103638372023-07-25 Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology Huss, Ralf Raffler, Johannes Märkl, Bruno Cancer Rep (Hoboken) Review BACKGROUND: The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re‐)activate the patient's immune system and direct it against the individual cancer in the most effective way. RECENT FINDINGS: Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune‐oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune‐cancer‐network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer‐assisted development and clinical validation of such digital biomarker. CONCLUSIONS: The successful implementation of AI‐supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into “precision pathology” delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a “precision oncology”. John Wiley and Sons Inc. 2023-02-22 /pmc/articles/PMC10363837/ /pubmed/36813293 http://dx.doi.org/10.1002/cnr2.1796 Text en © 2023 The Authors. Cancer Reports published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Huss, Ralf Raffler, Johannes Märkl, Bruno Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology |
title | Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology |
title_full | Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology |
title_fullStr | Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology |
title_full_unstemmed | Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology |
title_short | Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology |
title_sort | artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363837/ https://www.ncbi.nlm.nih.gov/pubmed/36813293 http://dx.doi.org/10.1002/cnr2.1796 |
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