File size: 4,520 Bytes
4843a0c da09b51 4843a0c 97d52d1 4843a0c 5fa352d da09b51 5fa352d da09b51 5fa352d da09b51 4843a0c da09b51 4c9acc4 4843a0c da09b51 5fa352d da09b51 5fa352d da09b51 5fa352d da09b51 5fa352d d923153 da09b51 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
---
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)*
---
## 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.
---
## 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.
---
## Available Model Variants
### 1. CT-only Models
- **CT_only**
- Input: Planning CT only
- Purpose: Baseline comparison against multi-modality approaches
---
### 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
---
### 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.
---
### 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.
---
## 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.
---
## Additional Resources
- **Codebase (training, inference, evaluation):**
https://github.com/xtie97/ISRT-CTV-AutoSeg
---
## 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}
|