radiomics-pathomics-fusion-agent

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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.

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

1. Pathomics Extraction: Extract histopathological features from WSI.

1. Clinical Integration: Combine imaging with clinical variables and genomics.

1. Treatment Response Prediction: Predict chemotherapy, immunotherapy response.

1. 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.

1. Segmentation: Tumor ROI extraction from radiology.

1. Radiomics: Extract 3D radiomic features.

1. Pathomics: Extract histopathology features via foundation models.

1. Fusion: Multimodal feature integration.

1. Prediction: Treatment response, survival, biomarker prediction.

1. 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:

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

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