WORC 3.7.0

Workflow for Optimal Radiomics Classification.

WORC: Workflow for Optimal Radiomics Classification (v3.7.0)

Information

Unit test	Documentation	PyPi	Citing WORC

Introduction

WORC (Workflow for Optimal Radiomics Classification) is an open-source Python package that provides an end-to-end pipeline for fully automatic optimization of radiomics models, facilitating easy and systematic data probing for radiomics signatures by automatically comparing and combining thousand radiomics methods.

🔍 Key Features

• ✅ End-to-End Pipeline: Everythong from imaging data and segmentations to performance evaluation.

• 🧪 Modular Design: Easily integrate custom classifiers, feature selectors, or workflows.

• 🏥 Domain Focused: Designed and tested on 30+ multicenter medical datasets.

• 📁 Multi-modal Input: Supports multiple image modalities, regions, custom features, and clinical data.

• 📊 Explainability Tools: Built-in feature ranking and visualization utilities.

• 🔁 Robust Optimization: Nested cross-validation and sample size0based confidence interval estimation.

• 🧬 Radiomics-Ready: Includes PyRadiomics-based feature extraction and harmonization.

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📦 Installation

WORC is tested on Python 3.11.5 on Ubuntu, Windows, and Mac. For detailed installation instructions, please check the ReadTheDocs installation guidelines.

The package can be installed through pip:

pip install WORC

Or directly from the repository for development:

git clone https://github.com/MStarmans91/WORC.git
cd WORC
pip install .

📚 Tutorial, documentation and dataset

The WORC tutorial is hosted at https://github.com/MStarmans91/WORCTutorial. For your first use, we recommend to start with the SimpleWORC tutorial.

The official documentation can be found at https://worc.readthedocs.io.

The publicly released WORC database is described in the following paper:

@article {Starmans2021WORCDatabase,
    author = {Starmans, Martijn P.A. and Timbergen, Milea J.M. and Vos, Melissa and Padmos, Guillaume A. and Gr{\"u}nhagen, Dirk J. and Verhoef, Cornelis and Sleijfer, Stefan and van Leenders, Geert J.L.H. and Buisman, Florian E. and Willemssen, Francois E.J.A. and Koerkamp, Bas Groot and Angus, Lindsay and van der Veldt, Astrid A.M. and Rajicic, Ana and Odink, Arlette E. and Renckens, Michel and Doukas, Michail and de Man, Rob A. and IJzermans, Jan N.M. and Miclea, Razvan L. and Vermeulen, Peter B. and Thomeer, Maarten G. and Visser, Jacob J. and Niessen, Wiro J. and Klein, Stefan},
    title = {The WORC database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies},
    elocation-id = {2021.08.19.21262238},
    year = {2021},
    doi = {10.1101/2021.08.19.21262238},
    URL = {https://www.medrxiv.org/content/early/2021/08/25/2021.08.19.21262238},
    eprint = {https://www.medrxiv.org/content/early/2021/08/25/2021.08.19.21262238.full.pdf},
    journal = {medRxiv}
}

The code to download the WORC database and reproduce our experiments can be found at https://github.com/MStarmans91/WORCDatabase.

📄 License

This package is covered by the open source APACHE 2.0 License.

When using WORC, please cite this repository and the paper describing WORC as follows:

@article{starmans2021reproducible,
   title          = {Reproducible radiomics through automated machine learning validated on twelve clinical applications},
   author         = {Martijn P. A. Starmans and Sebastian R. van der Voort and Thomas Phil and Milea J. M. Timbergen and Melissa Vos and Guillaume A. Padmos and Wouter Kessels and David    Hanff and Dirk J. Grunhagen and Cornelis Verhoef and Stefan Sleijfer and Martin J. van den Bent and Marion Smits and Roy S. Dwarkasing and Christopher J. Els and Federico Fiduzi and Geert J. L. H. van Leenders and Anela Blazevic and Johannes Hofland and Tessa Brabander and Renza A. H. van Gils and Gaston J. H. Franssen and Richard A. Feelders and Wouter W. de Herder and Florian E. Buisman and Francois E. J. A. Willemssen and Bas Groot Koerkamp and Lindsay Angus and Astrid A. M. van der Veldt and Ana Rajicic and Arlette E. Odink and Mitchell Deen and Jose M. Castillo T. and Jifke Veenland and Ivo Schoots and Michel Renckens and Michail Doukas and Rob A. de Man and Jan N. M. IJzermans and Razvan L. Miclea and Peter B. Vermeulen and Esther E. Bron and Maarten G. Thomeer and Jacob J. Visser and Wiro J. Niessen and Stefan Klein},
   year           = {2021},
   eprint         = {2108.08618},
   archivePrefix  = {arXiv},
   primaryClass   = {eess.IV}
}

@software{starmans2018worc,
  author       = {Martijn P. A. Starmans and Thomas Phil and Sebastian R. van der Voort and Stefan Klein},
  title        = {Workflow for Optimal Radiomics Classification (WORC)},
  year         = {2018},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3840534},
  url          = {https://github.com/MStarmans91/WORC}
}

For the DOI, visit .

Contact

We are happy to help you with any questions. Please sent us a mail or place an issue on the Github.

Optional extra packages and software

Besides the default installation, there are several optional packages you could install to support WORC.

Graphviz

WORC can draw the network and save it as a SVG image using graphviz. In order to do so, please make sure you install graphviz. On Ubuntu, simply run

apt install graphiv

On Windows, follow the installation instructions provided on the graphviz website. Make sure you add the executable to the PATH when prompted.

Elastix

Image registration is included in WORC through elastix and transformix. In order to use elastix, please download the binaries and place them in your fastr.config.mounts['apps'] path. Check the elastix tool description for the correct subdirectory structure. For example, on Linux, the binaries and libraries should be in "../apps/elastix/4.8/install/" and "../apps/elastix/4.8/install/lib" respectively.

Note: optionally, you can tell WORC to copy the metadata from the image file to the segmentation file before applying the deformation field. This requires ITK and ITKTools: see the ITKTools github for installation instructions.

XNAT

We use the XNATpy package to connect the toolbox to the XNAT online database platforms. You will only need this when you use the example dataset we provided, or if you want to download or upload data from or to XNAT. We advise you to specify your account settings in a .netrc file when using this feature for your own datasets, such that you do not need to input them on every request.