| name | multimodal-radpath-fusion-agent |
| description | AI-powered multimodal diagnostic fusion integrating radiology imaging (CT/MRI/PET), digital pathology (WSI), genomics, and clinical data for comprehensive cancer diagnosis and treatment planning. |
Multimodal Radiology-Pathology Fusion Agent
The Multimodal Radpath Fusion Agent integrates diverse clinical data sources including radiology imaging (CT, MRI, PET), digital pathology whole slide images, genomic profiling, and electronic health records using state-of-the-art multimodal deep learning for comprehensive cancer diagnosis, treatment response prediction, and prognostic modeling.
When to Use This Skill
- When integrating radiology and pathology for unified tumor assessment.
- For treatment response prediction using multimodal imaging.
- To predict molecular features from imaging (imaging genomics).
- When building comprehensive prognostic models.
- For tumor board decision support with AI second opinion.
Core Capabilities
-
Radiology-Pathology Fusion: Integrate macro and microscopic views.
-
Imaging-Genomics Correlation: Predict molecular features from imaging.
-
Treatment Response Prediction: Multi-modal response modeling.
-
Survival Prediction: Comprehensive prognostic models.
-
Tumor Characterization: Integrate phenotype from all modalities.
-
Clinical Decision Support: AI-assisted tumor board recommendations.
Supported Modalities
| Modality | Data Type | Features Extracted |
|---|
| CT | DICOM volumes | Radiomics, deep features |
| MRI | Multi-sequence DICOM | Texture, perfusion, ADC |
| PET | SUV maps | Metabolic features |
| H&E WSI | SVS/NDPI images | Histology, spatial patterns |
| IHC | Stained slides | Biomarker quantification |
| WES/WGS | VCF | Mutations, TMB, signatures |
| RNA-seq | Expression matrix | Pathway signatures |
| Clinical | EHR data | Demographics, labs, history |
Fusion Architectures
| Architecture | Method | Best For |
|---|
| AMRI-Net | Attention fusion | Radiology focus |
| PathOmCLIP | Contrastive learning | Path-omics alignment |
| SMuRF | Swin Transformer | Multi-region integration |
| MultiModal Transformer | Self-attention | All modalities |
| GNN Fusion | Graph networks | Spatial relationships |
Workflow
-
Data Ingestion: Collect radiology, pathology, genomics, clinical.
-
Preprocessing: Standardize each modality.
-
Feature Extraction: Extract modality-specific features.
-
Alignment: Temporal and spatial alignment of data.
-
Fusion: Multi-modal deep learning integration.
-
Prediction: Diagnosis, response, survival prediction.
-
Output: Integrated report with explanations.
Example Usage
User: "Integrate this lung cancer patient's CT scan, biopsy pathology, and genomic profiling for comprehensive assessment and treatment recommendation."
Agent Action:
python3 Skills/Clinical/Multimodal_Radpath_Fusion_Agent/multimodal_fusion.py \
--ct_dicom ct_chest/ \
--pet_dicom pet_scan/ \
--wsi_path biopsy.svs \
--genomic_vcf tumor_wes.vcf \
--rna_expression expression.tsv \
--clinical_ehr patient_data.json \
--task treatment_recommendation \
--cancer_type nsclc \
--output integrated_assessment/
Output Components
| Output | Description | Format |
|---|
| Integrated Diagnosis | Multi-modal classification | .json |
| Treatment Prediction | Response probabilities | .json |
| Survival Estimate | Prognostic curves | .json, .png |
| Feature Attribution | Modality importance | .json |
| Attention Maps | Visual explanations | .npy, .png |
| Clinical Report | Summary for tumor board | .pdf |
| Confidence Scores | Prediction uncertainty | .json |
Clinical Applications
| Application | Modalities | Performance |
|---|
| NSCLC IO Response | CT + H&E + PD-L1 | AUC 0.85 |
| HCC Treatment Selection | MRI + H&E + AFP | AUC 0.82 |
| Breast Neoadjuvant | MRI + H&E + HER2 | AUC 0.88 |
| HNSCC HPV/Prognosis | CT + H&E + p16 | AUC 0.89 |
| GBM Survival | MRI + H&E + MGMT | C-index 0.76 |
Imaging-Genomics Predictions
| Molecular Feature | Imaging Modality | Accuracy |
|---|
| EGFR mutation | CT | 75-80% |
| KRAS mutation | CT | 70-75% |
| PD-L1 expression | CT + H&E | 80-85% |
| MSI status | H&E | 85-90% |
| TMB level | H&E | 75-80% |
| HRD status | H&E | 78-83% |
AI/ML Components
Feature Extraction:
- 3D ResNet for CT/MRI volumes
- Vision Transformers for WSI
- Foundation models (CONCH, UNI)
Fusion Methods:
- Cross-attention mechanisms
- Multimodal transformers
- Contrastive multimodal learning
Prediction Models:
- Multi-task learning
- Survival analysis (DeepSurv)
- Uncertainty quantification
Prerequisites
- Python 3.10+
- PyTorch, transformers
- SimpleITK, OpenSlide
- Foundation model weights
- GPU with 32GB+ VRAM (recommended)
Related Skills
- Radiomics_Pathomics_Fusion_Agent - Imaging-specific fusion
- Pathology_AI/CONCH_Agent - Pathology foundation model
- Pan_Cancer_MultiOmics_Agent - Genomic integration
- Virtual_Lab_Agent - AI research coordination
Integration with Clinical Workflow
| Integration Point | System | Purpose |
|---|
| PACS | Radiology archive | Image retrieval |
| LIS | Pathology system | Slide access |
| EHR | Medical records | Clinical data |
| Tumor Board | MDT platform | Decision support |
| Reporting | Clinical reports | Documentation |
Special Considerations
- Data Alignment: Ensure temporal correspondence
- Missing Modalities: Handle incomplete multimodal data
- Privacy: HIPAA compliance for clinical integration
- Validation: Multi-site validation essential
- Explainability: Clinical trust requires interpretability
Explainability Methods
| Method | Output | Purpose |
|---|
| Attention Maps | Heatmaps | Important regions |
| SHAP Values | Feature importance | Modality contribution |
| GradCAM | Activation maps | Visual explanation |
| Counterfactuals | What-if analysis | Decision boundaries |
Quality Control
| QC Check | Threshold | Action |
|---|
| Image Quality | Score >0.7 | Flag for review |
| Data Completeness | >80% fields | Proceed or wait |
| Prediction Confidence | >0.6 | Report with confidence |
| Calibration | ECE <0.1 | Trust probabilities |
Author
AI Group - Biomedical AI Platform
