TCGA-GBM360: GBM360 aggressiveness maps for a subset of TCGA pathology slides

This collection contains algorithmically-derived spatial aggressiveness maps for a subset of microscopy images from the TCGA collections. The data was generated using the GBM360 algorithm developed by Olivier Gevaert lab at Stanford University. These spatial aggressiveness maps indicate the local aggressiveness of different tumor regions, with higher aggressiveness contributing to a worse overall survival, based on the analysis performed by the authors of the algorithm. The data included in this dataset were produced using the algorithm described in [1,2]. This dataset contains the annotations harmonized into DICOM Segmentation representation. This dataset is available from the NCI Imaging Data Commons (IDC), and can be explored interactively in the IDC Portal using this link: https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TCGA-GBM360 Specific files included in the record can be downloaded using the attached manifests. The suffix of the manifest indicates its content, which is the list of pointers to the public Google Cloud Storage (GCS) or Amazon Web Services (AWS) buckets containing the files included in the collection: -gcs.s5cmd: GCS-based manifest (to download the files described in the manifest, execute this command: pip install --upgrade idc-index && idc download manifest). -aws.s5cmd: AWS-based manifest (to download the files described in the manifest, execute this command: pip install --upgrade idc-index && idc download manifest). -dcf.dcf: Gen3-based manifest (see details in https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids). [1] Zheng, Y., Carrillo-Perez, F., Pizurica, M. et al. Spatial cellular architecture predicts prognosis in glioblastoma. Nat Commun 14, 4122 (2023). https://doi.org/10.1038/s41467-023-39933-0 [2] https://github.com/gevaertlab/GBM360