WORC: Workflow for Optimal Radiomics Classification

Welcome to the WORC documentation!

WORC is an open-source python package for the easy execution of end-to-end radiomics pipelines. Using automatic algorithm optimization, WORC automatically determines the optimal combination from a wide variety of radiomics methods to develop a signature on your dataset. Thereby, performing a radiomics study is effectively reduced to a black box with a push button, where you simply have to input your data and WORC will adapt the workflow to your application. Thus, WORC is especially suitable for the fast development of signatures and thus probing datasets for new biomarkers.

We aim to establish a general radiomics platform supporting easy integration of other tools. With our modular build and support of different software languages (Python, MATLAB, R, executables etc.), we want to facilitate and stimulate collaboration, standardisation and comparison of different radiomics approaches. By combining this in a single framework, we hope to find an universal radiomics strategy that can address various problems.

WORC is open-source (licensed under the Apache 2.0 license) and hosted on Github at https://github.com/MStarmans91/WORC

For support, go to the issues on the Gibhub page: https://github.com/MStarmans91/WORC/issues

To get yourself a copy, see the Installation chapter.

The official documentation can be found at WORC.readthedocs.io.

For Tutorials on WORC, both for beginner and advanced WORCflows, please see our Tutorial repository https://github.com/MStarmans91/WORCTutorial.

For more information regarding radiomics, we recommend the following book chapter:

The article on WORC is currently in press. WORC has been presented in the following:

WORC has been used in the following studies:

Milea Timbergen*, Martijn PA Starmans*, Melissa Vos, Michel Renckens, Dirk J Grünhagen, Geert JLH van Leenders, Wiro J Niessen, Cornelis Verhoef, Stefan Sleijfer, Stefan Klein, Jacob J Visser. “Radiomics of Gastrointestinal Stromal Tumors; Risk Classification Based on Computed Tomography Images–A Pilot Study.” European Journal of Surgical Oncology 2020

Melissa Vos*, MPA Starmans*, MJM Timbergen, SR van der Voort, GA Padmos, W Kessels, WJ Niessen, GJLH van Leenders, DJ Grünhagen, Stefan Sleijfer, C Verhoef, Stefan Klein, Jacob Johannes Visser. “Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI” British Journal of Surgery 2019

Martijn P. A. Starmans, Sebastian R. van der Voort, Razvan L. Miclea, Melissa Vos, Fatih Incekara, Milea J.M. Timbergen, Maarten M.J. Wijnenga, Guillaume A. Padmos, Wouter Kessels, G.H.J. van Leenders, George Kapsas, Martin J. van den Bent, Arnaud J.P.E. Vincent, Dirk J. Grünhagen, Cornelis Verhoef, Stefan Sleijfer, Jacob J. Visser, Marion Smits, Maarten, G. Thomeer, Wiro J. Niessen, and Stefan Klein. “Fully Automatic Construction of Optimal Radiomics Workflows .” Bio-Medical Engineering (BME) Conference 2019.

M. P. A. Starmans, R. Miclea, S. R. van der Voort, W. J. Niessen, S. Klein and M. G. Thomeer. “Classification of malignant and benign liver tumours using a radiomics approach.” European Conference of Radiology (ECR) 2019.

M. P. A. Starmans, A. Blazevic, S. R. van der Voort, T. Brabander, J. Hofland, W. J. Niessen, W. W. de Herder and S. Klein. “Prediction of surgery requirement in mesenteric fibrosis on CT using a radiomics approach.” European Conference of Radiology (ECR) 2019.

Jose M. Castillo T., Martijn P. A. Starmans, Ivo Schoots, Wiro J. Niessen, Stefan Klein, Jifke F. Veenland. “CLASSIFICATION OF PROSTATE CANCER: HIGH GRADE VERSUS LOW GRADE USING A RADIOMICS APPROACH.” IEEE International Symposium on Biomedical Imaging (ISBI) 2019.

M. P. A. Starmans, R. Miclea, S. R. van der Voort, W. J. Niessen, M. G. Thomeer and S. Klein. “Classification of malignant and benign liver tumors using a radiomics approach.” Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105741D (2018) .

Milea J.M. Timbergen*, Martijn P. A. Starmans*, Melissa Vos, Sebastian R. van der Voort, Guillaume A. Padmos, Wouter Kessels, Wiro J. Niessen, Geert J.L.H. van Leenders, Dirk J. Grünhagen, Stefan Sleijfer, Cornelis Verhoef, Stefan Klein and Jacob J. Visser. “Mutation stratification of desmoid-type fibromatosis using a Radiomics approach.” Desmoid Tumor Research Foundation (DTRF) 2018.

Melissa Vos*, Martijn P. A. Starmans*, Milea J.M. Timbergen, Sebastian R. van der Voort, Guillaume A. Padmos, Wouter Kessels, Wiro J. Niessen, Geert J.L.H. van Leenders, Dirk J. Grünhagen, Stefan Sleijfer, Cornelis Verhoef, Stefan Klein and Jacob J. Visser. “Differentiating well-differentiated liposarcomas from lipomas using a Radiomics approach.” Connective Tissue Oncology Society (CTOS) conference 2018.

Milea J.M. Timbergen*, Martijn P. A. Starmans*, Melissa Vos, Sebastian R. van der Voort, Guillaume A. Padmos, Wouter Kessels, Wiro J. Niessen, Geert J.L.H. van Leenders, Dirk J. Grünhagen, Stefan Sleijfer, Cornelis Verhoef, Stefan Klein and Jacob J. Visser. “Mutation stratification of desmoid-type fibromatosis using a Radiomics approach.” Connective Tissue Oncology Society (CTOS) conference 2018.

WORC is made possible by contributions from the following people: Martijn Starmans, Thomas Phil, and Stefan Klein

WORC Documentation

WORC User reference

WORC Developer Module reference

Indices and tables