Deep Learning of Cell Spatial Organizations Identifies Clinically Relevant Insights in Tissue Images
Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a novel cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (i.e. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.
| Errataetall: |
UpdateIn: Nat Commun. 2023 Dec 11;14(1):7872. - PMID 38081823 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
Research square - (2023) vom: 04. Juli |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Shidan [VerfasserIn] |
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Date Revised 25.12.2023 published: Electronic UpdateIn: Nat Commun. 2023 Dec 11;14(1):7872. - PMID 38081823 Citation Status PubMed-not-MEDLINE |
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| doi: |
10.21203/rs.3.rs-2928838/v1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM359622429 |
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| 520 | |a Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a novel cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (i.e. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies | ||
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| 700 | 1 | |a Yang, Donghan M |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xinyi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhan, Xiaowei |e verfasserin |4 aut | |
| 700 | 1 | |a Bishop, Justin |e verfasserin |4 aut | |
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