Glioblastoma MRI Dataset with Standardized Preprocessing, Expert-Validated Segmentation, and MGMT Profiling.
Authors
Affiliations (24)
Affiliations (24)
- Federal Neurosurgical Center, 132/1 Nemirovicha-Danchenko St, Novosibirsk, 630087, Russia.
- The Artificial Intelligence Research Center of Novosibirsk State University, 1 Pirogov St, Novosibirsk, 630090, Russia.
- Department of Neurosurgery, Städtisches Klinikum Karlsruhe, Karlsruher Neurozentrum, Karlsruhe, Germany.
- Department of Neurosurgery, Städtisches Klinikum Karlsruhe, Karlsruher Neurozentrum, Karlsruhe, Germany. [email protected].
- Division of Neurosurgery, University Hospital of Foggia, Foggia, Italy. [email protected].
- Department of Biomedical and Neuromotor Sciences (DIBINEM), "Alma Mater Studiorum" University of Bologna, Bologna, Italy.
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Programma Neurochirurgia Ipofisi-Pituitary Unit, Bologna, Italy.
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genova, 16132, Genova, Italy.
- Department of Neurosurgery, IRCCS Ospedale Policlinico San Martino, Genova, 16132, Genoa, Italy.
- Department of Neurosurgery, AOU Città della Salute e della Scienza, Turin, Italy.
- Department of Neurosurgery, University of Turin, Turin, Italy.
- Institute for Anthropomatics and Robotics, Karlsruhe Institute for Technology - KIT, Karlsruhe, Germany.
- Department of Radiology, Division of Neuroradiology, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany.
- Neurosurgery Department, Hospital Universitari Joan XXIII, Tarragona, Spain.
- Universitat Rovira i Virgili, Tarragona, Spain.
- Division of Medical Oncology, A.O.U. Consorziale Policlinico Di Bari, Bari, Italy.
- Unit of Medical Oncology and Biomolecular Therapy and CREATE Center for Research and Innovation Medicine, Department of Medical and Surgical Sciences, University of Foggia, Policlinico Riuniti, Foggia, Italy.
- Fondazione IRCCS Casa Sollievo della Sofferenza, UOC di Neurochirurgia, San Giovanni Rotondo, Italy.
- Neuroradiology Unit, Ospedale Maggiore, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
- Department of Biomedical Sciences, Dental Sciences and Morpho-functional Imaging, University of Messina, Messina, Italy.
- Division of Neurosurgery, Department of Translational Biomedicine and Neurosciences, University "Aldo Moro" of Bari, Bari, Italy.
- Interdisciplinary Department of Medicine, University of Bari "Aldo Moro" and Division of Medical Oncology, A.O.U. Consorziale Policlinico Di Bari, Bari, Italy.
- The Artificial Intelligence Research Center of Novosibirsk State University, 1 Pirogov St, Novosibirsk, 630090, Russia. [email protected].
- Division of Neurosurgery, University Hospital of Foggia, Foggia, Italy.
Abstract
Glioblastoma research increasingly relies on large, well-curated imaging datasets that combine standardized MRI data, accurate tumor segmentations, and molecular profiling. We constructed a multi-center dataset of preoperative MRI scans from 337 patients with histologically confirmed primary glioblastoma collected across eight hospitals. All cases include T1-weighted (pre- and post-contrast), T2-weighted, and FLAIR sequences. Images underwent systematic quality assessment, BIDS organization, defacing, skull stripping, and linear registration to the MNI152 template. Tumor segmentation was performed using a SegResNet CNN model following the BraTS labeling convention, with all masks reviewed and manually refined by neuroradiologists. MGMT promoter methylation status was determined for all patients. This dataset provides a robust, clinically representative resource for radiomics, deep learning, and radiogenomic research in glioblastoma, supporting concrete downstream tasks including automated segmentation benchmarking (mean Dice = 0.94) and MGMT methylation prediction (baseline ACC = 0.60). Its multi-center origin, comprehensive preprocessing, expert-refined segmentations, and complete MGMT annotations address limitations of existing datasets and support the development and validation of reproducible imaging biomarkers.