This application provides the prediction of the molecular subtype of de novo glioblastoma patients using baseline pre-operative multi-parametric MRI analysis [1].
REQUIREMENTS:
- Co-registered Multi-modal MRI: T1, T1-Gd, T2, T2-FLAIR, DTI-AX, DTI-FA, DTI-RAD, DTI-TR, DSC-PH, DSC-PSR, DSC-rCBV.
- Segmentation labels of the tumor sub-regions: Non-enhancing tumor core (Label=1), Enhancing tumor core (Label=4), Edema (Label=2), as well as the Ventricle (Label=7)
- Segmentation labels in a common atlas space: Non-enhancing tumor core (Label=1), Enhancing tumor core (Label=4), Edema (Label=2), as well as the Ventricle (Label=7)
- Clinical data: A csv file having patient's demographics (Note that the CSV file should be in ASCII format). Should have age (in first column) and molecular subtype (in second column) for training a new model, and age only for molecular subtype prediction of new patients.
- The data for each patient should be organized in the following directory structure. When running in the command-line, filenames must include words in BOLD to be identified as respective required files.
- Subject_ID
- features.csv
- CONVENTIONAL
- my_t1_file.nii.gz
- my_t2_file.nii.gz
- my_t1ce_file.nii.gz
- my_flair_file.nii.gz
- DTI
- my_axial_file.nii.gz
- my_fractional_file.nii.gz
- my_radial_file.nii.gz
- my_trace_file.nii.gz
- PERFUSION
- my_rcbv_file.nii.gz
- my_psr_file.nii.gz
- my_ph_file.nii.gz
- SEGMENTATION
- label_segmentation_file.nii.gz (in same space as above images)
- label_in_atlas_space.nii.gz (in common atlas space)
- The data of multiple patients should be organized in the above mentioned structure and reside under the same folder, e.g.:
- Input_Directory
- Subject_ID1
- Subject_ID2
- ...
- Subject_IDn
USAGE:
- Train New Model:
- "Select Subjects". Select the input directory that follows the folder structure described above.
- "Output". Select the folder where the trained model will be saved.
- A pop-up window appears displaying the completion of model building. (Time depends on the number of patients: ~2*Patients minutes).
- Use Existing Model:
- "Model Directory". Choose the directory of a saved model.
- "Test Subjects". Select the input directory that follows the folder structure described above.
- "Output". Select the output directory where a .csv file with the predicted molecular subtypes for all patients will be saved, and click on 'Confirm'.
- A pop-up window appears displaying the completion of subtype calculation. The window will also show the molecular subtype of the first subject in the Data_of_multiple_patients folder (runtime depends on the number of patients: ~2*patients minutes).
References:
- L.Macyszyn, H.Akbari, J.M.Pisapia, X.Da, M.Attiah, V.Pigrish, Y.Bi, S.Pal, R.V.Davuluri, L.Roccograndi, N.Dahmane. M.Martinez-Lage, G.Biros, R.L.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos. "Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques", Neuro Oncol. 18(3):417-25, 2016, DOI:10.1093/neuonc/nov127.