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Intracranial Implantation with Subsequent 3D In Vivo Bioluminescent Imaging of Murine Gliomas
Published on: November 6, 2011
Amsterdam IMAging and Clinical GliOma Dataset; IMAGO
Ivar J H G Wamelink1,2, Alle Meije Wink3,4, Niels Verburg5
1Amsterdam UMC Location Vrije Universiteit Amsterdam, Radiology & Nuclear Medicine Department, De Boelelaan 1117, Amsterdam, The Netherlands. i.j.wamelink@amsterdamumc.nl.
Scientific Data
|June 11, 2026
Summary
The IMAGO dataset offers 1,700 adult-type diffuse glioma patient MRI scans and clinical data. This rich resource supports research in synthetic imaging, tumor segmentation, and predicting glioma features.
Area of Science:
- Neuroimaging
- Oncology
- Medical Informatics
Background:
- Adult-type diffuse gliomas are a significant cause of brain tumors.
- Access to comprehensive, multi-modal datasets is crucial for advancing glioma research.
- The Amsterdam University Medical Centers IMAging in GliOma (IMAGO) dataset addresses this need.
Purpose of the Study:
- To introduce the IMAGO dataset, a large-scale collection of multimodal data for adult-type diffuse glioma.
- To provide researchers with a valuable resource for developing and validating AI models for glioma analysis.
- To facilitate advancements in glioma imaging, segmentation, and molecular feature prediction.
Main Methods:
- The IMAGO dataset comprises 1,700 adult-type diffuse glioma patients.
- Includes preoperative and postoperative structural MRI, diffusion-weighted MRI (ADC), and dynamic susceptibility contrast MRI (rCBV).
- Histological and molecular data (IDH, 1p/19q codeletion, WHO CNS5 grade) and tumor segmentations are also provided.
Main Results:
- The dataset contains detailed imaging and clinical information for 1,700 patients.
- Pre- and postoperative MRI sequences, ADC, and rCBV images are registered to MNI space.
- nnUnet-based tumor segmentations are available for a subset of cases.
Conclusions:
- The IMAGO dataset is a comprehensive resource for adult-type diffuse glioma research.
- It enables the creation of synthetic images, tumor segmentation, and prediction of histological/molecular features.
- This dataset will accelerate AI-driven advancements in neuro-oncology.