ArticleScientific data2024
A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Small Particles, Big Impact: Inorganic Nanotechnology for Glioblastoma.Molecules (Basel, Switzerland) · 2026Review
- BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification.Scientific data · 2026Article
- Radiomics machine learning model based on multi-sequence MRI for predicting Ki-67 expression level in glioblastoma.Frontiers in molecular biosciences · 2026Article
- A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development.Journal of imaging · 2025Review
- Current Treatment Paradigms for Advanced Melanoma with Brain Metastases.International journal of molecular sciences · 2025Review
- Review
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- A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors.Scientific data · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Brain metastases (BMs) and high-grade gliomas (HGGs) are the most common and aggressive types of malignant brain tumors in adults, with often poor prognosis and short survival. As their clinical symptoms and image appearances on conventional magnetic resonance imaging (MRI) can be astonishingly similar, their accurate differentiation based solely on clinical and radiological information can be very challenging, particularly for "cancer of unknown primary", where no systemic malignancy is known or found. Non-invasive multiparametric MRI and radiomics offer the potential to identify these distinct biological properties, aiding in the characterization and differentiation of HGGs and BMs. However, there is a scarcity of publicly available multi-origin brain tumor imaging data for tumor characterization. In this paper, we introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes anonymized DICOM files alongside processed FLAIR, T1-weighted, contrast-enhanced T1-weighted, T2-weighted sequences images, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting.
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Registered trials
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