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This repository contains the source code for Sybil, a neural network used for lung cancer prediction based on low-dose computed tomography (LDCT) scans.

For general usage, we recommend using the public Docker container which has been packaged with Ark. See the Ark wiki for more complete documentation.

Example scripts for bash, batch, and powershell: ark_predict.zip

Each of these scripts accepts a directory, host, and port as input. The script then submits every DICOM image in the input directory to the host, assuming that the ark server is running.

Sybil example data

End-to-End example using published container

# Run published container
docker run --rm -d -p 5000:5000 mitjclinic/sybil:latest

# Check container is running, get version
curl http://localhost:5000/info

# Download and unzip zip file with scripts
curl -L -o ark_predict.zip "https://github.com/reginabarzilaygroup/ark/files/14299465/ark_predict.zip"
tar -xf ark_predict.zip

# Download example data
curl -L -o sybil_example.zip "https://www.dropbox.com/scl/fi/covbvo6f547kak4em3cjd/sybil_example.zip?rlkey=7a13nhlc9uwga9x7pmtk1cf1c&dl=0"
tar -xf sybil_example.zip

# Submit images for prediction
./ark_predict.sh sybil_demo_data

Cite

@article{mikhael2023sybil,
  title={Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography},
  author={Mikhael, Peter G and Wohlwend, Jeremy and Yala, Adam and Karstens, Ludvig and Xiang, Justin and Takigami, Angelo K and Bourgouin, Patrick P and Chan, PuiYee and Mrah, Sofiane and Amayri, Wael and Juan, Yu-Hsiang and Yang, Cheng-Ta and Wan, Yung-Liang and Lin, Gigin and Sequist, Lecia V and Fintelmann, Florian J. and Barzilay, Regina},
  journal={Journal of Clinical Oncology},
  pages={JCO--22},
  year={2023},
  publisher={Wolters Kluwer Health}
}