Elena Filimonova, Augusto Leone, Francesco Carbone, Matteo Zoli, Jamil Rzaev, Mariya Schukina, Alessandro Carretta, Andrea Bianconi, Fabio Cofano, Alberto Morello, Daniele Armocida, Uwe Spetzger, Safwan Roumia, Veronica Di Napoli, Nicola Pio Fochi, Ruth Lau, Valeria Internò, Guido Giordano, Antonello Curcio, Arianna Rustici, Flavio Angileri, Diego Mazzatenta, Francesco Signorelli, Camillo Porta, Diego Garbossa, Minh Sao Khue Luu, Margaret Benedichuk, Ahsan Shakoor, Bair Tuchinov, Antonio Colamaria
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.