Cancer Imaging Phenomics Toolkit (CaPTk)  1.9.0
Brain Cancer: Glioblastoma EGFRvIII SVM Index

This application provides the detection of EGFRvIII mutation status of de novo glioblastoma patients by using baseline pre-operative multi-parametric MRI analysis [1].


  1. Co-registered Multimodal MRI: T1, T1-Gd, T2, T2-FLAIR, DTI-AX, DTI-FA, DTI-RAD, DTI-TR, DSC-PH, DSC-PSR, DSC-rCBV.
  2. Segmentation labels of the tumor sub-regions: Non-enhancing tumor core (Label=1), Enhancing tumor core (Label=4), as well as the Edema (Label=2)
  3. 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)
  4. 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 EGFRvIII status (in second column, binarized values like 0 and 1) for training a new model, and age only for mutation detection of new patients.
  5. 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
      1. features.csv
        • my_t1_file.nii.gz
        • my_t2_file.nii.gz
        • my_t1ce_file.nii.gz
        • my_flair_file.nii.gz
      3. DTI
        • my_axial_file.nii.gz
        • my_fractional_file.nii.gz
        • my_radial_file.nii.gz
        • my_trace_file.nii.gz
      4. PERFUSION
        • my_rcbv_file.nii.gz
        • my_psr_file.nii.gz
        • my_ph_file.nii.gz
        • my_label_file.nii.gz (in same space as above images)
        • my_atlas_file.nii.gz (in common atlas space)
  6. The data of simgle or multiple patients should be organized in the above mentioned structure and reside under the same folder, e.g.:
    • Input_Directory
      1. Subject_ID1
      2. Subject_ID2
      3. ...
      4. Subject_IDn


  • Use Existing Model
    1. "Model Directory". Choose the directory of a saved model.
    2. "Test Subjects". Select the input directory that follows the folder structure described above.
    3. "Output". Select the output directory where a .csv file with the mutation status for all patients will be saved, and click on 'Confirm'.The first and the second column of .csv will be subject's ID and distancce of the subject from the hyperplance of EGFRvIII model.
    4. A pop-up window appears displaying the completion of result calculation. The window will also show the detected mutation status of the first subject in the Data_of_multiple_patients folder (runtime depends on the number of patients: ~2*patients minutes).
    • This application is also available as with a stand-alone CLI for data analysts to build pipelines around, using the following example command:
      ${CaPTk_InstallDir}/bin/EGFRvIIIIndexPredictor.exe -t 1 -i C:/EGFRvIIIInputDirectory -m C:/EGFRvIIIInputModel -o C:/EGFRvIIIOutputDirectory


  1. H. Akbari, S. Bakas, J.M. Pisapia, M.P. Nasrallah, M. Rozycki, M. Martinez-Lage, J.J.D. Morrissette, N. Dahmane, D.M.O’Rourke, C. Davatzikos. "In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature", Neuro Oncol. 20(8):1068-1079, 2018