Brain Cancer: Pseudoprogression Infiltration Index

This application provides an estimate of the pseudo-progression after radiotherapy in glioblastoma patients, via multi-parametric MRI analysis, as shown in [1].

REQUIREMENTS:

1. Co-registered Multimodal MRI: T1, T1-Gd, T2, T2-FLAIR, DSC-MRI, DTI-AX, DTI-FA, DTI-RAD, DTI-TR. Ensure that these are the identified modalities in the drop-down menus next to each loaded image.
2. Segmentation label of the demarcated region of interest (Label=1) in a single NIfTI (.nii.gz) file.
3. The data for each patient should be organized in the following directory structure.
   • SubjectID
     1. CONVENTIONAL
        • "my_T1_file.nii.gz"
        • "my_T2_file.nii.gz"
        • "my_T1CE_file.nii.gz"
        • "my_FLAIR_file.nii.gz"
     2. DTI
        • "my_AX_file.nii.gz"
        • "my_FA_file.nii.gz"
        • "my_RAD_file.nii.gz"
        • "my_TR_file.nii.gz"
     3. PERFUSION
        • "my_PERFUSION_file.nii.gz"
     4. SEGMENTATION
        • "my_segmentation_file.nii.gz"
4. The data of multiple patients should be organized in the above mentioned structure and reside under the same folder, e.g.:
   • Data_of_multiple_patients
     1. Subject_ID1
     2. Subject_ID2
     3. ...
     4. Subject_IDn

USAGE:

• Pseudoprogression assessment on a batch of subjects.
  • "Train a new model":
    1. Select the "Training Directory", e.g., Data_of_multiple_patients (confirm the data follows the structure instructed above).
    2. Select the "Output Directory" where the trained model should be saved.
    3. Click on 'Confirm'.
    4. A pop-up window will confirm the completion of model training (~1.5*NoOfSubjects minutes).
    5. This application is also available as with a stand-alone CLI for data analysts to build pipelines around.
    6. NOTE: in the sample data, we are providing multiple duplicates of 5 unique subjects to show the training functionality at work; the model generated using these should NOT be used to generate results.

       PseudoProgressionEstimator.exe -t 0 -i C:/PseudoprogressionSubjects -o C:/PseudoprogressionModel
       

  • "Use existing model":
    1. Select the "Model Directory". Note that a model trained on a cohort of HUP can be found in ftp://www.nitrc.org/home/groups/captk/downloads/models/pseudoprogression.zip
    2. Select the "Test Directory", e.g., Data_of_multiple_patients (confirm the data follows the structure instructed above).
    3. Select the "Output Directory", where the user wants to save the infiltration maps.
    4. Click on 'Confirm'.
    5. A pop-up window will confirm the completion of assessment of pseudoprogression (~1.5*NoOfSubjects minutes).
    6. This application is also available as with a stand-alone CLI for data analysts to build pipelines around:

       PseudoProgressionEstimator.exe -t 1 -i C:/PseudoprogressionSubjects -o C:/PseudoprogressionOutput -m C:/PseudoprogressionModel
       

RESULT INTERPRETATION:

• 1st column: distance to hyperplane that classifies pseudo-progression versus the rest
• 2nd column: distance to hyperplane that classifies true-progression versus the rest

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Reference:

1. H.Akbari, S.Bakas, M.Martinez-Lage, M.Nasrallah, M.Rozycki, S.Rathore, G.Shukla, S.Mohan, M.Bilello, C.Davatzikos, "Quantitative radiomics and machine learning to distinguish true progression from pseudoprogression in patients with GBM", ASNR 56th Annual Meeting, 2018.