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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

Prediction using published container

A pre-built container is available on Docker Hub at https://hub.docker.com/r/mitjclinic/sybil.

The following commands are an example of how to download and run the container, as well as submit images for prediction.

Mac or Linux

From a terminal:

# 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=1"
tar -xf sybil_example.zip

# Submit images for prediction
# Mac/Linx
./ark_predict.sh sybil_demo_data

Windows

Open PowerShell in administrative mode, and enter:

Set-ExecutionPolicy RemoteSigned

When prompted, confirm with "Y". The rest of the commands do not need a window with administrative access.

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

# Check that container is running, and get version
curl.exe http://localhost:5000/info

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

# Download example data
curl.exe -L -o sybil_demo_data.zip "https://www.dropbox.com/scl/fi/covbvo6f547kak4em3cjd/sybil_example.zip?rlkey=7a13nhlc9uwga9x7pmtk1cf1c&dl=1"
tar -xf sybil_demo_data.zip

# Submit images for prediction
.\ark_predict.ps1 sybil_demo_data

Expected Response

Processing can take 2-10 minutes depending on the hardware.

{
  "data": {
    "predictions": [
      [
        [
          0.021628819563619374,
          0.03857256315036462,
          0.07191945816622261,
          0.07926975188037134,
          0.09584583525781108,
          0.13568094038444453
        ]
      ],
      null
    ]
  },
  "message": null,
  "metadata": null,
  "runtime": "169.00s",
  "statusCode": 200
}

The numbers under "prediction" are listed in order for year 1, year 2, ... through year 6. These represent the estimated cumulative probability of the patient contracting lung cancer within the time frame. That is, the probabilities are cumulative.

Video Tutorial

Short video tutorials are also available: Using Sybil in Docker Container (Video Tutorials).zip

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}
}