portpy 0.0.1

First open-source radiation treatment planning system in Python

What is PortPy?

Note: The package is at its early stages of development (version 0.0.3) and we are now collecting feedback from researchers to further refine the data structure and the main functionality. We are expecting to have a stable version 1.xx around March 2023. We would love to hear your feedback.

PortPy (Planning and Optimization for Radiation Therapy) is a community effort to develop the first opensource python library to facilitate the development and clinical translation of radiotherapy cancer treatment planning algorithms. PortPy includes:

1. Research-ready data and code for benchmarking, reproducibility, and community-driven development.
2. Interface to an open-source optimization package CVXPy for easy/quick prototyping and out-of-the-box access to commercial/opensource optimization engines (e.g., Mosek, Gorubi, CPLEX, IPOPT).
3. Visualization modules to visualize relevant plan information (e.g, dose volume histograms, dose distribution, fluence map).
4. Evaluation modules to quantify plan quality with respect to established clinical metrics (e.g., RTOG metrics, dose conformality, tumor control probability, normal tissue control probability).

Data

Data needed for optimization and algorithm development (e.g., a set of beams/beamlets/voxels, dose contribution of each beamlet to each voxel) are provided for a set of pre-specified machine parameters (e.g., beam/collimator/couch angles). We will initially provide these for a set of publicly available datasets from TCIA. We hope to expand our dataset in the future. The data needed for optimization is extracted from the research version of Eclipse^TM treatment planning system (Varian Medical Systems) using its API.

You can download the sample patient data here.

Create a directory named './data' in the current project directory and copy the downloaded file to it, e.g ./data/Lung_Patient_1

Installing PortPy

1. Installing using pip

pip install portpy-photon

2. Installing using conda

conda install -c conda-forge portpy-photon

3. Installing from source

• Clone this repository:

  git clone https://github.com/PortPy-Project/PortPy-Photon.git
  cd portpy_photon
  

• You need to install the dependencies in either a python virtual environment or anaconda environment. Instructions for setting up in python virtual environment are as follows:

  Install all the dependencies present in requirements.txt:

  python3 -m venv venv
  source venv/bin/activate
  (venv) pip install -r requirements.txt
  

To better understand the PortPy functionality, we recommend running an example script eg_1_basics.py for creating and visualizing a sample IMRT plan.

License

PortPy code is distributed under Apache 2.0 with Commons Clause license, and is available for non-commercial academic purposes.

Team

PortPy is a community project initiated at Memorial Sloan Kettering Cancer Center. It is currently developed and maintained by:

Name	Expertise	Institution
Masoud Zarepisheh	Treatment Planning and Optimization	MSK
Saad Nadeem	Computer Vision and AI in Medical Imaging	MSK
Gourav Jhanwar	Algorithm Design and Development	MSK
Mojtaba Tefagh	Mathematical Modeling and Reinforcement Learning	SUT
Vicki Taasti	Physics and Planning of Proton Therapy	MAASTRO
Sadegh Alam	Adaptive Treatment Planning and Imaging	Cornell
Seppo Tuomaala	Eclispe API Scripting	VARIAN