What is PortPy?

PortPy, short for Planning and Optimization for Radiation Therapy, represents a collective effort to establish an open-source Python library dedicated to advancing the development and clinical implementation of cancer radiotherapy treatment planning algorithms. This initiative encompasses planning methodologies for Intensity Modulated Radiation Therapy (IMRT), Volumetric Modulated Arc Therapy (VMAT), along with other emerging modalities. PortPy provides clinical-grade data and coding resources that foster benchmarking, reproducibility, and community development.

Note: In the near future, we plan to launch an educational YouTube channel to assist researchers new to this field. Meanwhile, we suggest reviewing relevant literature review papers (Zarepisheh et al. 2021, Breedveld et al. 2019, Ehrgott et al. 2010) and watching YouTube videos (Edelman competition, Varian IMRT, Elekta VMAT).

Contents

• Why we created PortPy?
• What can you do with PortPy?
• Quick Start
• How to contribute?
• Data
• Installation
• Team

Why we created PortPy?

A key limitation of existing open-source packages in radiotherapy treatment planning is their inability to objectively evaluate new treatment planning techniques against current clinical practices. This limitation stems from the difficulty in fully replicating clinical environments, such as detailed linear accelerator configurations and commercial dose calculation engines. Consequently, plans generated using novel techniques, such as AI-based automated treatment planning, cannot be objectively compared with clinical plans used in patient treatment. To address this limitation, we are integrating PortPy with commercial treatment planning systems (TPSs). PortPy is already compatible with the Varian Eclipse TPS, and we plan to expand compatibility to additional TPSs in the future. It's crucial to note that PortPy can be used independently of any TPS, but accessing a TPS is essential for final evaluation within the TPS (see above figure).

Above figure highlights the inspiration for developing PortPy, drawing on successful open-source practices from the AI and computer science communities. Tools like PyTorch and TensorFlow, along with benchmark datasets such as ImageNet and algorithms like AlexNet, have transformed AI and data science. Our aim is to emulate this success, providing researchers with comprehensive resources (PortPy integration with commercial TPS and other renowned open-source tools such as 3DSlicer and CVXPy, a benchmark dataset (featuring 50 curated lung patients with expertly selected beams, all necessary optimization data, and benchmark IMRT plans created using our in-house automated planning system, ECHO), and benchmark algorithms, including Mixed Integer Programming (MIP) algorithms for achieving globally optimal solutions.

What can you do with PortPy?

PortPy facilitates the design, testing, and clinical validation of new treatment planning algorithms. This includes both cutting-edge AI-based models and traditional optimization techniques. The above figure illustrates the PortPy design and its three main modules: “Data Management”, “Plan Generation”, and “Plan Evaluation”, which are discussed below. We recommend reviewing our Jupyter Notebooks examples for a more comprehensive understanding of these modules.

1. Data Management

   • This module provides access to the curated benchmark PortPy dataset, which currently comprises data from 50 lung patients. It allows researchers to test and develop their algorithms using the same dataset (see basic_tutorial.ipynb notebook)
   • The available data includes: 1) CT images and contours, 2) all necessary data for optimization extracted from Eclipse using its API (version 16.1), 3)expert-selected beams for each patient, 4) an IMRT plan for each patient, generated using our in-house automated planning system, ECHO (YouTube Video, Paper). More information about data can be found in Data section.
   • In the current version, you can only work with the benchmark dataset provided in this PortPy repo and cannot use your own dataset for now. We will address this problem in the near future

2. Plan Generation

   • This module facilitates the generation of treatment plans using either classical optimization methods or emerging AI-based techniques
   • For optimization tasks, PortPy has been integrated with CVXPy, a widely-used open-source package. CVXPy enables the high-level formulation of optimization problems and offers out-of-the-box access to a range of free (e.g., SCIP, SCIPY) and commercial (e.g., MOSEK, CPLEX, GUROBI) optimization engines (available for free for research purposes) (see basic_tutorial.ipynb notebook)
   • PortPy.AI module is equipped with essential functionalities for AI-based planning. These include data access, data pre-processing, model training and testing, and patient-specific 3D dose prediction (see dose_prediction_pipeline.ipynb notebook)
   • The availability of both optimization and AI-based planning modules within PortPy allows researchers to not only compare these techniques but also explore their complementary aspects

3. Plan Visualization and Evaluation

   • Basic built-in visualization tools (e.g., DVH, dose distribution) are integrated into PortPy
   • Enhanced visualizations are available through the integration with the popular open-source 3DSlicer package (see 3d_slicer_integration.ipynb notebook)
   • PortPy IMRT plans with optimal fluence can be imported into Eclipse for leaf sequencing and final clinical evaluations (see eclipse_integration.ipynb)
   • PortPy IMRT/VMAT plans with optimal control points can be imported into any FDA approved TPS ( e.g. Eclipse, Raystation) using DICOM RT Plan file for final clinical evaluations (see TPS-integration.ipynb). It should be noted that some discrepancies are anticipated with TPSs other than Eclipse, due to the fact that our current data are derived from Eclipse, and there may exist variations in dose calculation methods between Eclipse and other TPSs.
   • Plans can also be evaluated within PortPy using well-established clinical protocols (e.g., Lung 2Gyx30, see basic_tutorial.ipynb)
   • Future updates will include more standardized RTOG metrics and outcome models (TCP/NTCP)

Quick Start

1. To grasp the primary features of PortPy, we highly recommend exploring the basic_tutorial.ipynb notebook
2. To understand how to import a PortPy plan into Eclipse for final evaluations, browse through the eclipse_integration.ipynb notebook
3. To create dicom RT Plan file from PortPy plan and import into FDA approved commercial TPS (e.g. Eclipse, Raystation), please browse through TPS-integration.ipynb notebook
4. To learn about enhanced visualization techniques using the 3D-Slicer package, refer to the 3d_slicer_integration.ipynb notebook
5. For algorithm benchmarking, the global optimal solutions are provided for non-convex optimization problems resulting from beam angle optimization beam_orientation_optimization.ipynb, incorporating DVH constraints dvh_constraint_optimization.ipynb, and VMAT optimization vmat_optimization.ipynb using the mixed-integer programming on down-sampled data.
6. If you encounter computational challenges with large-scale optimization problems, you can opt for down-sampling the voxels/beamlets, as illustrated in the down_sampling notebook, or further sparsify the influence matrix, as demonstrated in the inf_matrix_sparsification notebook.
7. To learn about creating a final deliverable plan using AI based model, please refer to dose_prediction_pipeline.ipynb notebook.

How to contribute?

As illustrated in the above figure, PortPy organization includes "PortPy", which is the current repository, and PortPy extensions, which are the repositories developed using the PortPy as a platform. To maintain the lightweight nature and user-friendliness of PortPy modules, our aim is to include only fundamental functionalities, along with benchmark data and algorithms in the PortPy repo, and establish separate repositories for other projects, similar to what we've done for projects like LowDimRT and ECHO VMAT.

If you're interested in contributing to existing PortPy modules or wish to create a new module, we encourage you to contact us first. This will help ensure that our objectives and priorities are aligned. If you use PortPy to build your own package, you're welcome to host your package within the PortPy-Project orgainization. Alternatively, you can host your package on your own GitHub page. In this case, please inform us so that we can fork it and feature it under the PortPy-Project organization. For those keen on creating a logo for their repository, we offer the option to customize our pre-designed logo.

Data

PortPy equips researchers with a robust benchmark patient dataset, sourced from the FDA-approved Eclipse commercial treatment planning system through its API. This dataset embodies all necessary elements for optimizing various machine configurations such as beam angles, aperture shapes, and leaf movements. It includes

1. Dose Influence Matrix (AKA dose deposition matrix, dij matrix): The dose contribution of each beamlet to each voxel,
2. Beamlets/Voxels Details: Detailed information about the position and size of beamlets/voxels,
3. Expert-Selected Benchmark Beams: An expert clinical physicist has carefully selected benchmark beams, providing reference beams for comparison and benchmarking,
4. Benchmark IMRT Plan: A benchmark IMRT plan generated using our in-house automated treatment planning system called ECHO (YouTube Video, Paper). This plan serves as a benchmark for evaluating new treatment planning algorithms.
5. Benchmark Clinical Criteria: A set of clinically relevant mean/max/DVH criteria for plan evaluation. Currently, this set encompasses only the Lung 2Gy×30 protocol but will be expanded in the future to more protocols as well as TCP/NTCP evaluation functions.

To access these resources, users are advised to download the latest version of the dataset, which can be found here. Subsequently, create a directory titled './data' in the current project directory and transfer the downloaded file into it. For example, ./data/Lung_Phantom_Patient_1. We have adopted the widely-used JSON and HDF5 formats for data storage. HDFViwer can be utilized to view the contents of the HDF5 files.

Note: Initially, we will utilize a lung dataset from TCIA. The original DICOM CT images and structure sets are not included in the PortPy dataset and need to be directly downloaded from the TCIA. Users can fetch the TCIA collection ID and the TCIA subject ID for each PortPy patient using the get_tcia_metadata() method in PortPy and subsequently download the data from TCIA (see eclipse_integration.ipynb)

Installation

1. Install using pip:

   • Run the command 'pip install portpy'

2. Install using conda:

   • Run the command 'conda install -c conda-forge portpy'

3. Install from source:

   • Clone this repository using 'git clone https://github.com/PortPy-Project/PortPy.git'

   • Navigate to the repository with 'cd portpy'

   • Install the dependencies within a Python virtual environment or Anaconda environment. To set up in a Python virtual environment, install all the dependencies specified in requirements.txt as follows:

     • Create the virtual environment with 'python3 -m venv venv'
     • Activate the environment with 'source venv/bin/activate'
     • Install the requirements using '(venv) pip install -r requirements.txt'

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
Seppo Tuomaala	Eclispe API Scripting	VARIAN

License

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

Reference

If you find our work useful in your research or if you use parts of this code please cite our AAPM'23 abstract :

@article{jhanwar2023portpy,
  title={Portpy: An Open-Source Python Package for Planning and Optimization in Radiation Therapy Including Benchmark Data and Algorithms},
  author={Jhanwar, Gourav and Tefagh, Mojtaba and Taasti, Vicki T and Alam, Sadegh R and Tuomaala, Seppo and Nadeem, Saad and Zarepisheh, Masoud},
  journal={AAPM 65th Annual Meeting & Exhibition},
  year={2023}
}