radiomics-pathomics-fusion-agent

by FreedomIntelligence

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Download radiomics-pathomics-fusion-agent.zip

SKILL.md About

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---
name: 'radiomics-pathomics-fusion-agent'
description: 'AI-powered multimodal fusion of radiology (CT/MRI/PET) and pathology (H&E/IHC) imaging with clinical and genomic data for comprehensive cancer diagnostics and treatment prediction.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---

# Radiomics Pathomics Fusion Agent

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 to Use This Skill

* 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.
* For AI-powered second opinion integrating all imaging modalities.

## Core Capabilities

1. **Cross-Modal Fusion**: Integrate radiology and pathology features using attention.

2. **Radiomics Extraction**: Compute 3D texture, shape, intensity features from CT/MRI.

3. **Pathomics Extraction**: Extract histopathological features from WSI.

4. **Clinical Integration**: Combine imaging with clinical variables and genomics.

5. **Treatment Response Prediction**: Predict chemotherapy, immunotherapy response.

6. **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 |

## Workflow

1. **Input**: CT/MRI DICOM, pathology WSI, clinical data, optional genomics.

2. **Segmentation**: Tumor ROI extraction from radiology.

3. **Radiomics**: Extract 3D radiomic features.

4. **Pathomics**: Extract histopathology features via foundation models.

5. **Fusion**: Multimodal feature integration.

6. **Prediction**: Treatment response, survival, biomarker prediction.

7. **Output**: Integrated predictions, attention maps, explanations.

## Example Usage

**User**: "Predict immunotherapy response for this lung cancer patient using their CT scan and biopsy pathology."

**Agent Action**:
```bash
python3 Skills/Oncology/Radiomics_Pathomics_Fusion_Agent/fusion_predict.py \
    --ct_dicom ct_scan/ \
    --wsi_path biopsy.svs \
    --clinical_data patient_clinical.json \
    --genomic_data tumor_wes.vcf \
    --task immunotherapy_response \
    --cancer_type nsclc \
    --fusion_method attention \
    --output fusion_prediction/
```

## Radiomic Feature Categories

| Category | Features | Count |
|----------|----------|-------|
| Shape | Volume, surface area, sphericity | 14 |
| First-Order | Mean, variance, skewness, entropy | 18 |
| GLCM | Contrast, correlation, homogeneity | 24 |
| GLRLM | Run length, gray level emphasis | 16 |
| GLSZM | Zone size, gray level variance | 16 |
| GLDM | Dependence features | 14 |
| NGTDM | Texture features | 5 |
| **Total** | | **~107** |

## Pathomics Feature Categories

| Category | Source | Features |
|----------|--------|----------|
| Nuclear | Segmentation | Size, shape, texture |
| Cellular | Detection | Density, clustering |
| Tissue | Architecture | Glandular, stromal ratios |
| Foundation Model | CONCH, TITAN, UNI | Deep embeddings |
| Spatial | Graph analysis | Neighborhood patterns |

## Output Components

| Output | Description | Format |
|--------|-------------|--------|
| Prediction | Response/outcome probability | .json |
| Confidence | Prediction uncertainty | .json |
| Attention Maps | Cross-modal importance | .npy, .png |
| Feature Importance | Shapley values | .csv |
| ROI Highlights | Predictive regions | DICOM-SEG, GeoJSON |
| Report | Clinical summary | .pdf |

## Clinical Applications

| Application | Modalities Used | Performance |
|-------------|-----------------|-------------|
| NSCLC Immunotherapy | CT + H&E | AUC 0.82-0.88 |
| HCC Survival | MRI + H&E | C-index 0.78 |
| Breast Neoadjuvant | MRI + H&E | AUC 0.85 |
| HNSCC HPV/Response | CT + H&E | AUC 0.89 |
| CRC MSI Prediction | CT + H&E | AUC 0.86 |

## AI/ML Components

**Radiomics Pipeline**:
- PyRadiomics for feature extraction
- 3D-CNN for learned features
- Transformer for volumetric analysis

**Pathomics Pipeline**:
- Foundation models (CONCH, UNI, TITAN)
- MIL (Multiple Instance Learning) for WSI
- Graph networks for spatial patterns

**Fusion Models**:
- Cross-attention transformers
- Multimodal variational autoencoders
- Contrastive learning for alignment

## Prerequisites

* Python 3.10+
* PyRadiomics, SimpleITK
* OpenSlide, HistoEncoder
* PyTorch, transformers
* CONCH/TITAN model weights
* GPU with 16GB+ VRAM

## 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

## Multimodal Integration Strategies

| Strategy | Description | Use Case |
|----------|-------------|----------|
| Feature-Level | Combine extracted features | Limited data |
| Embedding-Level | Fuse latent representations | Moderate data |
| Decision-Level | Ensemble predictions | Interpretability |
| End-to-End | Joint training | Large data |

## Special Considerations

1. **Data Alignment**: Ensure imaging from same timepoint
2. **Missing Modalities**: Handle incomplete multimodal data
3. **Class Imbalance**: Balance training across outcomes
4. **Interpretability**: Attention maps for clinical trust
5. **Validation**: External multi-site validation essential

## Quality Control

| QC Check | Threshold | Action |
|----------|-----------|--------|
| CT coverage | >90% tumor | Rescan if needed |
| WSI quality | Blur score <X | Re-scan slide |
| Segmentation | Dice >0.85 | Manual review |
| Feature stability | ICC >0.8 | Robust features only |

## Regulatory Considerations

| Aspect | Status |
|--------|--------|
| FDA Clearance | Individual modality tools cleared |
| Multimodal Fusion | Research use only (RUO) |
| Clinical Integration | PACS/LIS integration pathways |
| Explainability | Required for clinical adoption |

## Author

AI Group - Biomedical AI Platform