https://github.com/agr78/raddbs-qsm
Deep brain stimulation outcome prediction using radiomics on quantitative susceptibility maps
https://github.com/agr78/raddbs-qsm
cornell cornell-university dbs deep-brain-stimulation machine-learning qsm quantitative-susceptibility-mapping radiomic-features radiomics radiomics-analysis radiomics-extraction radiomics-features radiomics-signatures wcm weill
Last synced: 8 months ago
JSON representation
Deep brain stimulation outcome prediction using radiomics on quantitative susceptibility maps
- Host: GitHub
- URL: https://github.com/agr78/raddbs-qsm
- Owner: agr78
- Created: 2023-03-08T19:44:38.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-10-08T03:16:28.000Z (8 months ago)
- Last Synced: 2025-10-08T03:26:46.547Z (8 months ago)
- Topics: cornell, cornell-university, dbs, deep-brain-stimulation, machine-learning, qsm, quantitative-susceptibility-mapping, radiomic-features, radiomics, radiomics-analysis, radiomics-extraction, radiomics-features, radiomics-signatures, wcm, weill
- Language: Python
- Homepage:
- Size: 153 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Radiomic Deep Brain Stimulation Prediction
with Quantitative Susceptibility Mapping (RadDBS-QSM)
This repository hosts the following articles
>_Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting
Deep Brain Stimulation Outcomes in Parkinson’s Disease_
>[published](https://pubmed.ncbi.nlm.nih.gov/40965145/) in [Neurosurgery](https://journals.lww.com/neurosurgery/pages/default.aspx)
>
>_Radiomic Prediction of Parkinson’s Disease Deep Brain Stimulation Surgery Outcomes using Quantitative Susceptibility Mapping and Label Noise Compensation_
>[published](https://www.brainstimjrnl.com/article/S1935-861X(25)00166-4/fulltext) in [Brain Stimulation](https://www.brainstimjrnl.com/)
and several [conference papers](https://alexandragroberts.com/publications/#radiomic).
## Contents
Demonstration code can be found in [`main.ipynb`](https://github.com/agr78/RadDBS-QSM/blob/main/src/jupyter/main.ipynb)
Radiomic features can be found in [`npy`](https://github.com/agr78/RadDBS-QSM/tree/main/data/npy/rp)
Customizable extraction code is located in [`extract.py`](https://github.com/agr78/RadDBS-QSM/blob/main/src/jupyter/extract.py)
## Summary
A radiomic model based on presurgical quantitative susceptibility maps (QSM) is used to predict patient outcomes to deep brain stimulation (DBS) surgery for the treatment of Parkinson's disease.
Model overview.
This project presents a framework to:
* Extract radiomic features for input into a regression model to predict post-surgical motor improvement.
* Incorporate clinical variables such as age, sex, etc.
* Provide a novel label noise compensation technique improving outcome prediction.
## Installation
Clone the repository with
```
git clone https://github.com/agr78/RadDBS-QSM.git
```
Navigate to the repository
```
cd RadDBS-QSM
```
Run the setup script
```
source ./install.sh
```
Wait...then open the Jupyter notebook in the `RadDBS-QSMenv` environment
```
jupyter notebook ./src/jupyter/main.ipynb
```
## Notes
* This tool was developed for use with [QSM](https://mriquestions.com/quantitative-susceptibility.html), but can be used with other contrasts.
* If the QSM has not been reconstructed, [this repository](https://github.com/agr78/mSMV?tab=readme-ov-file#summary) provides code to obtain the whole brain susceptibility.
* If manual region-of-interest masks are not available, [this repository](https://github.com/agr78/mSMV/blob/atlas/README.md) provides bash scripts to create a sample atlas and register individual cases.
## Publications
If this code is used, please cite the following:
> [Neurosurgery Article](https://doi.org/10.1227/neu.0000000000003721): A. G. Roberts et al., "Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting Deep Brain Stimulation Outcomes in Parkinson’s Disease, 2025, DOI: 10.1227/neu.0000000000003721
>
## BibTex
```bibtex
@article{Roberts_RadDBS-QSM_2025,
title = "Technical feasibility of quantitative susceptibility mapping
radiomics for predicting deep brain stimulation outcomes in
Parkinson disease",
author = "Roberts, Alexandra G and Zhang, Jinwei and Tozlu, Ceren and
Romano, Dominick and Akkus, Sema and Kim, Heejong and Sabuncu,
Mert R and Spincemaille, Pascal and Li, Jianqi and Wang, Yi and
Wu, Xi and Kopell, Brian H",
journal = "Neurosurgery",
month = sep,
year = 2025,
keywords = "Deep brain stimulation; Machine learning; Parkinson disease;
Quantitative susceptibility mapping; Radiomics; Regression",
language = "en"
}
```
## Contact
Please direct questions to [Alexandra G. Roberts](https://github.com/agr78) at agr78@cornell.edu.