README.md

---
tags:
- radiation oncology
- medical imaging
- deep learning
- pediatric oncology
pipeline_tag: image-segmentation
---

# Multi-Modality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma

**Authored by**: Tie, X., Milgrom, S.A., Lo, A.C., Charpentier, A.-M., LaRiviere, M.J., Maqbool, D., Cho, S.Y., Kelly, K.M., Hodgson, D., Castellino, S.M., Hoppe, B.S., Bradshaw, T.J.

📄 **Related Publication**:  
[Multi-Modality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma](https://www.sciencedirect.com/science/article/pii/S0360301625065927)  
*International Journal of Radiation Oncology · Biology · Physics (Red Journal)*

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## Model Overview

This repository hosts deep learning models developed for **automated clinical target volume (CTV) delineation** in **involved-site radiation therapy (ISRT)** for **high-risk pediatric Hodgkin lymphoma**.

All models were trained and evaluated using imaging data from the [**Children’s Oncology Group (COG) AHOD1331 phase III clinical trial**](https://www.nejm.org/doi/full/10.1056/NEJMoa2206660), a large, multi-institutional pediatric lymphoma dataset. The models are designed to integrate **longitudinal, multi-modality imaging** (i.e., baseline and interim PET/CT and planning CT images) to predict CTVs for radiation treatment planning. 

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## Input Modalities

Depending on the model variant, inputs may include:

- **Post-Chemotherapy Planning CT**
- **Baseline PET/CT (PET1)**  
- **Interim PET/CT (PET2)** (after 2 cycles of chemotherapy)

All PET/CT images are co-registered to the planning CT using either **rigid** or **deformable** registration, depending on the model configuration.

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## Available Model Variants

### 1. CT-only Models
- **CT_only**
  - Input: Planning CT only
  - Purpose: Baseline comparison against multi-modality approaches

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### 2. Multi-Modality Early Fusion Models
- **Early_fusion**
  - Inputs: Planning CT + baseline PET/CT + interim PET/CT
  - Fusion strategy: Early fusion (channel-wise concatenation at input)
  - Registration: Deformable registration for all modalities

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### 3. Multi-Modality Late Fusion Models
- **Late_fusion**
  - Inputs: Planning CT + baseline PET/CT + interim PET/CT
  - Fusion strategy: Late fusion using architecture-specific feature integration
  - Registration: Deformable registration for all modalities

### Note that each variant has three models for ensemble.

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### 4. Ablation Study Models (SwinUNETR)

Additional SwinUNETR models trained as part of ablation experiments are provided to assess the impact of imaging inputs and registration strategies:

- **PET_1_2_rigid**
  - Inputs: Planning CT + baseline PET/CT + interim PET/CT
  - Registration: Rigid registration

- **PET_1_deform**
  - Inputs: Planning CT + baseline PET/CT (no interim PET/CT)
  - Registration: Deformable registration

- **PET_1_rigid**
  - Inputs: Planning CT + baseline PET/CT (no interim PET/CT)
  - Registration: Rigid registration

Each ablation folder contains both **early-fusion** and **late-fusion** SwinUNETR model weights.

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## Intended Use

These models are intended for **research use only**.  
They are designed to serve as **automated initial CTV contours** to support ISRT planning workflows and **must be reviewed and edited by radiation oncologists** prior to any clinical application.

The models are **not approved for clinical decision-making** and have not undergone regulatory clearance.

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## Additional Resources

- **Codebase (training, inference, evaluation):**  
  https://github.com/xtie97/ISRT-CTV-AutoSeg

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## Citation

If you use these models in your research, please cite the associated publication:

```bibtex
@article{TIE2025,
title = {Multi-Modality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma},
journal = {International Journal of Radiation Oncology*Biology*Physics},
year = {2025},
issn = {0360-3016},
doi = {https://doi.org/10.1016/j.ijrobp.2025.12.005},
url = {https://www.sciencedirect.com/science/article/pii/S0360301625065927},
author = {Xin Tie and Sarah A. Milgrom and Andrea C. Lo and Anne-Marie Charpentier and Michael J. LaRiviere and Danyal Maqbool and Steve Y. Cho and Kara M Kelly and David Hodgson and Sharon M. Castellino and Bradford S. Hoppe and Tyler J. Bradshaw}