MR Radiomics Platform, MRP V4.0
Developed by Chia-Feng Lu @ 2017-2019, firstname.lastname@example.org
MRP Latest update: 2019.7.16
Major Function List of MRP V4.0 (Download Matlab codes)
[Note] Microsoft Excel is required for the output of radiomic features in an xls/xlsx format. Memory larger than 16 GB is recommended.
- DICOM read and sort for MRI, CT, and PET images, optimized for brain, breast, and chest imaging.
- Cross-modality image co-registration and interpolation.
- Multi-modality operation of region of interest (ROI) and thresholding.
- Series images crop, zoom, window/contrast adjustment, and print.
- Computation of Radiomic features, including intensity-based, geometry-based, textural analyses, and wavelet decomposition for each MR modality (CET1, T2W, T2 FLAIR, ADC, Cp/rCBV, Ktrans/K2 maps). The computations on CT and PET SUV maps are also workable.
- Output extracted Radiomic features as Excel sheets.
- [Not released] Correlation and multivariate linear regression analyses between image features and gene expression of tumors.
- General introduction [01:51]
- Data preparation and DICOM import [09:41]
- Image coregistration and resolution adjustment [10:47]
- Multi-modality operation of region of interest (ROI) and thresholding [19:50]
- Extraction of Radiomic Features (incuding wavelet decomposition) [08:33]
Three-Level Machine-Learning Model in Glioma
Developed by Chia-Feng Lu @ 2017-2018, email@example.com
Architecture of Three-Level Machine-Learning Model (Please see the Figure 1 in the published paper)
- Classification Model for LGG vs. GBM (Download the trained model, threshold=0; Download the MATLAB script to retrain the model).
- Classification Model for IDH status in GBM (Download the trained model, threshold=-0.63; Download the MATLAB script to retrain the model).
- Classification Model for IDH status in LGG (Download the trained model, threshold=1.60; Download the MATLAB script to retrain the model).
- Classification Model for 1p/19q status in IDH-mutant LGG (Download the trained model, threshold=0; Download the MATLAB script to retrain the model).
* This model is pre-trained based on the TCGA-GBM and TCGA-LGG data collection (214 subjects from The Cancer Imaging Archive, http://www.cancerimagingarchive.net/), and is tested on the Taiwanese glioma dataset (30 subjects) and REMBRANDT collection (40 subjects, https://wiki.cancerimagingarchive.net/display/Public/REMBRANDT).