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Access the CBICA Image Processing Portal and create an account if you haven’t yet. Account creation may take a few days after submitting the form.
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Click on Application Categories -> Analysis -> GBM Survival ReSPOND 2023 Model.
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Enter the input and click on the Submit Job button.
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Required fields:
- User description: The text input in this field will be displayed in the output results report. It is not a model input.
- Age of patient in year
- Image input type (see image input below)
- T1, T1-POST, T2, T2-FLAIR image input. This can be either a
- DICOM series (upload all files per series) (Input type 1)
- Un-preprocessed NIfTI (Input type 1)
- BraTS pipeline preprocessed NIfTI in SRI space (co-registered, skull-stripped) (input type 2)
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Optional parameters:
- Series to use for BrainMAGE brain mask [T1 or T1-POST]: By default, the software will use the T1-POST image to create the brain mask. When the T1-POST image is cropped, the pipeline will benefit from using the T1 mask.
- Brain Mask image: User provided binary brain mask in SRI space. Labels are 1: brain; 0: non-brain. When provided, the pipeline will skip the brain mask generation and use this mask instead.
- Tumor Segmentation Image: User provided tumor segmentation mask in SRI space. Tumor segmentation labels should follow the BraTS convention, which includes enhancing tumor (ET — label 4), the peritumoral edema (ED — label 2), and the necrotic and non-enhancing tumor core (NCR/NET — label 1), as described both in the BraTS 2012-2013 TMI paper (10.1109/TMI.2014.2377694) and in the latest BraTS summarizing paper (arXiv:1811.02629). When provided, the pipeline will skip the tumor segmentation and use this mask instead.
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Results
- Please note that the pipeline could take anywhere from 30min to a day to generate results, depending on CBICA cluster traffic.
- When complete, users will be able to download all results in a zip file from this site.
- Results folder will include the prediction report, as well as the preprocessed images, brain mask, tumor segmentation, extracted features, and the Survival Prediction Index (SPI)