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General information

pyanno4rt is a Python package for conventional and outcome prediction model-based inverse photon and proton treatment plan optimization, including radiobiological and machine learning (ML) models for tumor control probability (TCP) and normal tissue complication probability (NTCP). It leverages state-of-the-art local and global solution methods to handle both single- and multi-objective (un)constrained optimization problems, thereby covering a number of different problem designs. To summarize roughly, the following functionality is provided:

Installation

You can install the latest distribution via:

pip install pyanno4rt

You can check the latest source code via:

git clone https://github.com/pyanno4rt/pyanno4rt.git

pyanno4rt has two main classes which provide a code-based and a UI-based interface:

Base class import for CLI/IDE

from pyanno4rt.base import TreatmentPlan

GUI import

from pyanno4rt.gui import GraphicalUserInterface

Development

pyanno4rt is open for new contributors of all experience levels. Please get in contact with us (see "Help and support") to discuss the format of your contribution.

Note: the "docs" folder on Github includes example files with CT/segmentation data and the photon dose-influence matrix for the TG-119 case, a standard test phantom which can be used for development. You will find more realistic patient data e.g. in the CORT dataset1 or the TROTS dataset2.

Help and support

To cite this repository:

@misc{pyanno4rt2024,
  title = {{pyanno4rt}: python-based advanced numerical nonlinear optimization for radiotherapy},
  author = {Ortkamp, Tim and Jäkel, Oliver and Frank, Martin and Wahl, Niklas},
  year = {2024},
  howpublished = {\url{http://github.com/pyanno4rt/pyanno4rt}}
}