FAIR-Radiomics
Findable(F), Accessible(A), Interoperability(I) and Reuse (R) -- Radiomics
FAIR-Radiomics tool is able to calculate radiomic feature complying with FAIR principles: (i) radiomics data and extraction details could be published with a Findable(F) and unique identifier; (ii) radiomics data and metadata are described with radiomics ontology, which make them accessible(A) and understandable by machines and humans; (iii) data uses a formal, standardized and applicable ontology for knowledge representation, which makes interoperability(I) among multi-centres possible; (iv) data offers explicit information on provenance and licenses for reuse (R).
Citation
The publication of FAIR-Radiomics is coming soon. Please cite the webpage when you use it for academic research.
Disclaimer
FAIR-Radiomics is still under development. Although we have tested and evaluated the workflow under many different situations, errors and bugs still happen unfortunately. Please use it cautiously. If you find any, please contact us and we would fix them ASAP.
Prerequisites
FAIR-Radiomicsis dependent on several tools and packages that are listed below.
- Anaconda python 3 version, which includes python and hundreds of popular data science packages and the conda package and virtual environment manager for Windows, Linux, and MacOS.
- Pyradiomics - radiomic extractor.
- ...
Getting Started
Execute:
python3 FAIR-Radiomics.py
License
FAIR-Radiomics may not be used for commercial purposes. This package is freely available to browse, download, and use for scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License.
Developers
- Zhenwei Shi1
- [Leonard Wee]1
- [Andre Dekker]1
1Department of Radiation Oncology (MAASTRO Clinic), GROW-School for Oncology and Development Biology, Maastricht University Medical Centre, The Netherlands.