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:
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¶
- Introduction
- Quick start guide
- User Manual
- Configuration
- Radiomics Features
- Additional functionality
- FAQ
- Installation
- Execution
- My experiment crashed, where to begin looking for errors?
- Error:
WORC.addexceptions.WORCValueError: First column in the file
given to SimpleWORC().labels_from_this_file(**) needs to be named Patient.
- Error:
WORC.addexceptions.WORCKeyError: 'No entry found in labeling
for feature file .../feat_out_0.hdf5.'
- Error:
File "...\lib\site-packages\numpy\lib\function_base.py", line 4406,
`` in delete keep[obj,] = False``IndexError: arrays used as indices must be of integer (or boolean) type
- Developer documentation
- Resource File Formats
- Changelog
- 3.3.1 - 2020-07-31
- 3.3.0 - 2020-07-28
- 3.2.2 - 2020-07-14
- 3.2.1 - 2020-07-02
- 3.2.0 - 2020-06-26
- 3.1.4 - 2020-05-26
- 3.1.3 - 2020-01-24
- 3.1.2 - 2019-12-09
- 3.1.1 - 2019-11-28
- 3.1.0 - 2019-10-16
- 3.0.0 - 2019-05-08
- 2.1.3 - 2019-04-08
- 2.1.2 - 2019-04-02
- 2.1.1 - 2019-02-15
- 2.1.0 - 2018-08-09
- 2.0.0 - 2018-02-13
- 1.0.0rc1 - 2017-05-08
WORC User reference¶
WORC Developer Module reference¶
- WORC Package
WORC
PackageWORC
Moduleaddexceptions
Module- Subpackages