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radiomics-pathomics-fusion-agent
Maintainer FreedomIntelligence · Last updated April 1, 2026
Extract radiomic and pathomic features and integrate for predictive modeling.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/radiomics-pathomics-fusion-agent
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- The Radiomics Pathomics Fusion Agent integrates multimodal medical imaging data from radiology (CT, MRI, PET) and digital pathology (H&E, IHC whole slide images) with clinical and genomic data using deep learning fusion architectures. It enables comprehensive cancer phenotyping, treatment response prediction, and prognostic modeling.
- When predicting treatment response using multimodal imaging.
- For comprehensive tumor phenotyping combining macro and micro views.
- To identify imaging biomarkers correlated with genomic features.
- When building prognostic models from combined radiology-pathology.
Source Doc
Excerpt From SKILL.md
Core Capabilities
-
Cross-Modal Fusion: Integrate radiology and pathology features using attention.
-
Radiomics Extraction: Compute 3D texture, shape, intensity features from CT/MRI.
-
Pathomics Extraction: Extract histopathological features from WSI.
-
Clinical Integration: Combine imaging with clinical variables and genomics.
-
Treatment Response Prediction: Predict chemotherapy, immunotherapy response.
-
Survival Prediction: Multi-modal prognostic modeling.
Supported Imaging Modalities
| Modality | Features Extracted | Resolution |
|---|---|---|
| CT | Texture, shape, density | Volumetric 3D |
| MRI | Multi-sequence, perfusion | Volumetric 3D |
| PET | SUV, metabolic features | Volumetric 3D |
| H&E WSI | Nuclear, tissue architecture | 40x magnification |
| IHC WSI | Marker quantification | 20-40x |
| Multiplexed IF | Spatial protein patterns | Subcellular |
Fusion Architectures
| Architecture | Method | Strengths |
|---|---|---|
| Early Fusion | Concatenate features | Simple, baseline |
| Late Fusion | Combine predictions | Modular |
| Attention Fusion | Cross-modal attention | Interpretable |
| Multimodal Transformer | Self-attention across modalities | State-of-art |
| Graph Fusion | GNN for relationships | Spatial awareness |
Use cases
- When predicting treatment response using multimodal imaging.
- For comprehensive tumor phenotyping combining macro and micro views.
- To identify imaging biomarkers correlated with genomic features.
- When building prognostic models from combined radiology-pathology.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- Pathology_AI/CONCH_Agent - Pathology foundation model
- Radiology_AI agents - Modality-specific analysis
- Pan_Cancer_MultiOmics_Agent - Genomic integration
- TMB_Estimation_Agent - Tumor mutational burden
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