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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2512.08572 |
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| _version_ | 1866914189993312256 |
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| author | Schüllerqvist, Olle Edgren Baumann, Jens Lindblad, Joakim Nordling, Love Mezheyeuski, Artur Micke, Patrick Sladoje, Nataša |
| author_facet | Schüllerqvist, Olle Edgren Baumann, Jens Lindblad, Joakim Nordling, Love Mezheyeuski, Artur Micke, Patrick Sladoje, Nataša |
| contents | The tumor microenvironment (TME) has emerged as a promising source of prognostic biomarkers. To fully leverage its potential, analysis methods must capture complex interactions between different cell types. We propose HiGINE -- a hierarchical graph-based approach to predict patient survival (short vs. long) from TME characterization in multiplex immunofluorescence (mIF) images and enhance risk stratification in lung cancer. Our model encodes both local and global inter-relations in cell neighborhoods, incorporating information about cell types and morphology. Multimodal fusion, aggregating cancer stage with mIF-derived features, further boosts performance. We validate HiGINE on two public datasets, demonstrating improved risk stratification, robustness, and generalizability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08572 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Cells to Survival: Hierarchical Analysis of Cell Inter-Relations in Multiplex Microscopy for Lung Cancer Prognosis Schüllerqvist, Olle Edgren Baumann, Jens Lindblad, Joakim Nordling, Love Mezheyeuski, Artur Micke, Patrick Sladoje, Nataša Computer Vision and Pattern Recognition The tumor microenvironment (TME) has emerged as a promising source of prognostic biomarkers. To fully leverage its potential, analysis methods must capture complex interactions between different cell types. We propose HiGINE -- a hierarchical graph-based approach to predict patient survival (short vs. long) from TME characterization in multiplex immunofluorescence (mIF) images and enhance risk stratification in lung cancer. Our model encodes both local and global inter-relations in cell neighborhoods, incorporating information about cell types and morphology. Multimodal fusion, aggregating cancer stage with mIF-derived features, further boosts performance. We validate HiGINE on two public datasets, demonstrating improved risk stratification, robustness, and generalizability. |
| title | From Cells to Survival: Hierarchical Analysis of Cell Inter-Relations in Multiplex Microscopy for Lung Cancer Prognosis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.08572 |