Cancer Imaging Phenomics Toolkit (CaPTk)  1.6.1
Brain Cancer: Glioblastoma Infiltration Index (Recurrence)

This application provides a probability map of deeply infiltrating tumor in the peritumoral edema/invasion region that largely agrees with subsequent recurrence in de novo glioblastoma patients, via multi-parametric MRI analysis, as shown in [1-3].

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 labels of the tumor sub-regions in a single NIfTI (.nii.gz) file: Non-enhancing tumor core (Label=1), Enhancing tumor core (Label=4), Edema (Label=2)
  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"
        • "my_near_region_file.nii.gz" (only for training a new model)
        • "my_far_region_file.nii.gz" (only for training a new model)
  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:

  • Infiltration prediction on loaded subject.
    1. Load the required images in CaPTk and correctly assign the modality label in the drop-down menu.
    2. Load the segmentation labels of the tumor sub-regions from a single NIfTI (.nii.gz) file. The labels included in the file should represent the Non-enhancing tumor core (Label=1), Enhancing tumor core (Label=4), and Edema (Label=2).
    3. 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/recurrence.zip
    4. Select the "Output Directory" and click on "Confirm".
    5. The result is saved in the output folder and also loaded in the list of modalities (within ~2 minutes).
  • Infiltration prediction 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.
        RecurrenceEstimator.exe -t 0 -i C:/RecurrenceSubjects -o C:/RecurrenceModel
        
    • "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/recurrence.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 infiltration map calculations (~1.5*NoOfSubjects minutes).
      6. This application is also available as with a stand-alone CLI for data analysts to build pipelines around:
        RecurrenceEstimator.exe -t 1 -i C:/RecurrenceSubjects -o C:/RecurrenceOutput -m C:/RecurrenceModel
        



References:

  1. H.Akbari, L.Macyszyn, X.Da, R.L.Wolf, M.Bilello, R.Verma, D.M.O'Rourke, C.Davatzikos, "Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity", Radiology. 273(2):502-10, 2014. DOI:10.1148/radiol.14132458
  2. H.Akbari, L.Macyszyn, J.Pisapia, X.Da, M.Attiah, Y.Bi, S.Pal, R.Davuluri, L.Roccograndi, N.Dahmane, R.Wolf, M.Bilello, D.M.O'Rourke, C.Davatzikos, "Survival Prediction in Glioblastoma Patients Using Multi-parametric MRI Biomarkers and Machine Learning Methods", American Society of Neuroradiology, O-525:2042-2044, 2015. (http://www.asnr.org/sites/default/files/proceedings/2015_Proceedings.pdf)
  3. H.Akbari, L.Macyszyn, X.Da, M.Bilello, R.L.Wolf, M.Martinez-Lage, G.Biros, M.Alonso-Basanta, D.M.O'Rourke, C.Davatzikos. "Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma", Neurosurgery. 78(4):572-80, 2016. DOI:10.1227/NEU.0000000000001202