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
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 |
Workflow
Input: CT/MRI DICOM, pathology WSI, clinical data, optional genomics.
Segmentation: Tumor ROI extraction from radiology.
Radiomics: Extract 3D radiomic features.
Pathomics: Extract histopathology features via foundation models.
Fusion: Multimodal feature integration.
Prediction: Treatment response, survival, biomarker prediction.
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
- Data Alignment: Ensure imaging from same timepoint
- Missing Modalities: Handle incomplete multimodal data
- Class Imbalance: Balance training across outcomes
- Interpretability: Attention maps for clinical trust
- 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
1---2name: radiomics-pathomics-fusion-agent3description: 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.4---56<!--7# COPYRIGHT NOTICE8# This file is part of the "Universal Biomedical Skills" project.9# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>10# All Rights Reserved.11#12# This code is proprietary and confidential.13# Unauthorized copying of this file, via any medium is strictly prohibited.14#15# Provenance: Authenticated by MD BABU MIA1617-->18192021# Radiomics Pathomics Fusion Agent2223The **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.2425## When to Use This Skill2627* When predicting treatment response using multimodal imaging.28* For comprehensive tumor phenotyping combining macro and micro views.29* To identify imaging biomarkers correlated with genomic features.30* When building prognostic models from combined radiology-pathology.31* For AI-powered second opinion integrating all imaging modalities.3233## Core Capabilities34351. **Cross-Modal Fusion**: Integrate radiology and pathology features using attention.36372. **Radiomics Extraction**: Compute 3D texture, shape, intensity features from CT/MRI.38393. **Pathomics Extraction**: Extract histopathological features from WSI.40414. **Clinical Integration**: Combine imaging with clinical variables and genomics.42435. **Treatment Response Prediction**: Predict chemotherapy, immunotherapy response.44456. **Survival Prediction**: Multi-modal prognostic modeling.4647## Supported Imaging Modalities4849| Modality | Features Extracted | Resolution |50|----------|-------------------|------------|51| CT | Texture, shape, density | Volumetric 3D |52| MRI | Multi-sequence, perfusion | Volumetric 3D |53| PET | SUV, metabolic features | Volumetric 3D |54| H&E WSI | Nuclear, tissue architecture | 40x magnification |55| IHC WSI | Marker quantification | 20-40x |56| Multiplexed IF | Spatial protein patterns | Subcellular |5758## Fusion Architectures5960| Architecture | Method | Strengths |61|--------------|--------|-----------|62| Early Fusion | Concatenate features | Simple, baseline |63| Late Fusion | Combine predictions | Modular |64| Attention Fusion | Cross-modal attention | Interpretable |65| Multimodal Transformer | Self-attention across modalities | State-of-art |66| Graph Fusion | GNN for relationships | Spatial awareness |6768## Workflow69701. **Input**: CT/MRI DICOM, pathology WSI, clinical data, optional genomics.71722. **Segmentation**: Tumor ROI extraction from radiology.73743. **Radiomics**: Extract 3D radiomic features.75764. **Pathomics**: Extract histopathology features via foundation models.77785. **Fusion**: Multimodal feature integration.79806. **Prediction**: Treatment response, survival, biomarker prediction.81827. **Output**: Integrated predictions, attention maps, explanations.8384## Example Usage8586**User**: "Predict immunotherapy response for this lung cancer patient using their CT scan and biopsy pathology."8788**Agent Action**:89```bash90python3 Skills/Oncology/Radiomics_Pathomics_Fusion_Agent/fusion_predict.py \91 --ct_dicom ct_scan/ \92 --wsi_path biopsy.svs \93 --clinical_data patient_clinical.json \94 --genomic_data tumor_wes.vcf \95 --task immunotherapy_response \96 --cancer_type nsclc \97 --fusion_method attention \98 --output fusion_prediction/99```100101## Radiomic Feature Categories102103| Category | Features | Count |104|----------|----------|-------|105| Shape | Volume, surface area, sphericity | 14 |106| First-Order | Mean, variance, skewness, entropy | 18 |107| GLCM | Contrast, correlation, homogeneity | 24 |108| GLRLM | Run length, gray level emphasis | 16 |109| GLSZM | Zone size, gray level variance | 16 |110| GLDM | Dependence features | 14 |111| NGTDM | Texture features | 5 |112| **Total** | | **~107** |113114## Pathomics Feature Categories115116| Category | Source | Features |117|----------|--------|----------|118| Nuclear | Segmentation | Size, shape, texture |119| Cellular | Detection | Density, clustering |120| Tissue | Architecture | Glandular, stromal ratios |121| Foundation Model | CONCH, TITAN, UNI | Deep embeddings |122| Spatial | Graph analysis | Neighborhood patterns |123124## Output Components125126| Output | Description | Format |127|--------|-------------|--------|128| Prediction | Response/outcome probability | .json |129| Confidence | Prediction uncertainty | .json |130| Attention Maps | Cross-modal importance | .npy, .png |131| Feature Importance | Shapley values | .csv |132| ROI Highlights | Predictive regions | DICOM-SEG, GeoJSON |133| Report | Clinical summary | .pdf |134135## Clinical Applications136137| Application | Modalities Used | Performance |138|-------------|-----------------|-------------|139| NSCLC Immunotherapy | CT + H&E | AUC 0.82-0.88 |140| HCC Survival | MRI + H&E | C-index 0.78 |141| Breast Neoadjuvant | MRI + H&E | AUC 0.85 |142| HNSCC HPV/Response | CT + H&E | AUC 0.89 |143| CRC MSI Prediction | CT + H&E | AUC 0.86 |144145## AI/ML Components146147**Radiomics Pipeline**:148- PyRadiomics for feature extraction149- 3D-CNN for learned features150- Transformer for volumetric analysis151152**Pathomics Pipeline**:153- Foundation models (CONCH, UNI, TITAN)154- MIL (Multiple Instance Learning) for WSI155- Graph networks for spatial patterns156157**Fusion Models**:158- Cross-attention transformers159- Multimodal variational autoencoders160- Contrastive learning for alignment161162## Prerequisites163164* Python 3.10+165* PyRadiomics, SimpleITK166* OpenSlide, HistoEncoder167* PyTorch, transformers168* CONCH/TITAN model weights169* GPU with 16GB+ VRAM170171## Related Skills172173* Pathology_AI/CONCH_Agent - Pathology foundation model174* Radiology_AI agents - Modality-specific analysis175* Pan_Cancer_MultiOmics_Agent - Genomic integration176* TMB_Estimation_Agent - Tumor mutational burden177178## Multimodal Integration Strategies179180| Strategy | Description | Use Case |181|----------|-------------|----------|182| Feature-Level | Combine extracted features | Limited data |183| Embedding-Level | Fuse latent representations | Moderate data |184| Decision-Level | Ensemble predictions | Interpretability |185| End-to-End | Joint training | Large data |186187## Special Considerations1881891. **Data Alignment**: Ensure imaging from same timepoint1902. **Missing Modalities**: Handle incomplete multimodal data1913. **Class Imbalance**: Balance training across outcomes1924. **Interpretability**: Attention maps for clinical trust1935. **Validation**: External multi-site validation essential194195## Quality Control196197| QC Check | Threshold | Action |198|----------|-----------|--------|199| CT coverage | >90% tumor | Rescan if needed |200| WSI quality | Blur score <X | Re-scan slide |201| Segmentation | Dice >0.85 | Manual review |202| Feature stability | ICC >0.8 | Robust features only |203204## Regulatory Considerations205206| Aspect | Status |207|--------|--------|208| FDA Clearance | Individual modality tools cleared |209| Multimodal Fusion | Research use only (RUO) |210| Clinical Integration | PACS/LIS integration pathways |211| Explainability | Required for clinical adoption |212213## Author214215AI Group - Biomedical AI Platform216217218<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
FridrichMethod