- Authors
- Motivation
- Features
- Dependencies
- Installation
- Usage and Examples
- License
- How to Cite
- References
Odette Rios-Ibacache
Contact email: odette.riosibacache@mail.mcgill.ca
Website: www.kildealab.com
The scattered nature of health data, along with the lack of standardization and interoperability, limits the potential of Artificial Intelligence (AI) incorporating medical images and radiomics to automate outcomes assessment in radiotherapy (RT) treatments. Establishing a standardized lexicon and data structure could enhance multicenter clinical studies. Our goal is to structure patient data relevant to RT research and create a knowledge base (KB), a machine-readable repository, with an ontology as a domain, including radiomics and medical images. We aim to identify the essential data elements needed to encode radiomics and dosiomics information and develop an ontology. We are building our study on Minimal Common Oncology Data Elements (mCODE), an international initiative to improve interoperability by establishing a core set of structured data elements. We built an extension to link patients' medical image data, radiomics, and dosiomics with their health records. A review of the existing literature on the standardization of radiomics and dosiomics methods was conducted to include the minimum parameters that would impact their acquisition. We included data elements recommended by the Image Biomarker Standardisation Initiative (IBSI) guidelines. We developed a feature-extractor module, mCODE-MOSAICO, which converts and stores the elements for our extension, automatically extracting the radiomics and dosiomics features from all the interested Region Of Interests (ROIs).
- To perfome the installation you should clone the latest version from GitHub. Please note that your Python installation should be 3.6 or later.
git clone https://github.com/kildealab/mCODE-MOSAICO
- To install the dependecies
cd mCODE-MOSAICO
pip install -r requirements.txt
- To import the package in Jupyter Notebook or Python3 file
import sys
sys.path.append('/path/to/the/folder/mCODE-MOSAICO/utils')
/path/to/patient/directories/ ├── 📁patient_id │ ├── 📁medical_images │ ├── 📁 date_modality │ ├──📄image_study.json │ ├──📄acquisition_properties.json │ ├──📄modality_properties.json │ └──📄date_modality.nrrd │ ├── ... │ ├── 📁RT_plans │ ├── 📁 date_RT │ ├── 📁radiomics │ ├── 📁 ROI_radiomics │ ├── 📁 date_modality │ ├──📄seg_ROI.nrrd │ ├──📄seg_ROI_radiomics.json │ └──📁 voxel_based │ ├──📄feature1.nrrd │ ├──📄feature2.nrrd │ └── ... │ ├── 📁 ROI2_radiomics │ ├── 📁 date_modality | ├──📄seg_ROI2.nrrd │ ├──📄seg_ROI2_radiomics.json │ └──📁 voxel_based │ ├──📄feature1.nrrd │ ├──📄feature2.nrrd │ └── ... │ └── 📁dosiomics │ ├── 📁 ROI_dosiomics │ ├── 📁 date_RT │ ├──📄seg_ROI.nrrd │ ├──📄seg_ROI_dosiomics.json │ └──📁 voxel_based │ ├──📄feature1.nrrd │ ├──📄feature2.nrrd │ └── ... │ ... ├── 📁patient_id N | └── ...
To extract only the radiomics for a single patient and a single ROI
python extract_only_radiomics patient ROI
To extract only the radiomics for all the patient and all the available ROIs
python extract_only_radiomcis all all
To extract only the dosiomics for a single patient and a single ROI
python extract_only_dosiomics patient ROI
To extract only the dosiomics for all the patient and all the available ROIs
python extract_only_dosiomics all all
To extract only the dosimetric or dvh factors for a single patient and a single ROI
python extract_only_dvh_factors patient ROI
To extract only the dosimetric or dvh factors for all the patient and all the available ROIs
python extract_only_dvh_factors all all
This project is provided under the GNU General Public License version 3 (GPLv3) to preserve open-source access to any derivative works. See the LICENSE file for more information.
- Traverso, A., Wee, L., Dekker, A., & Gillies, R. (2018). Repeatability and Reproducibility of Radiomic Features: A Systematic Review. International journal of radiation oncology, biology, physics, 102(4), 1143–1158. https://doi.org/10.1016/j.ijrobp.2018.05.053
- Vallières, M., Zwanenburg, A., Badic, B., Cheze Le Rest, C., Visvikis, D., & Hatt, M. (2018). Responsible Radiomics Research for Faster Clinical Translation. Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 59(2), 189–193. https://doi.org/10.2967/jnumed.117.200501
- Zwanenburg, Alex, Stefan Leger, Martin Vallières, and Steffen Löck. "Image Biomarker Standardisation Initiative." ArXiv, (2016). Accessed June 18, 2025. https://doi.org/10.1148/radiol.2020191145.