MOTUM
A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors
Brain MRI of patients with high-grade glioma or brain metastases from lung, breast, ovarian, gastric and melanoma primaries, shared to support tumor segmentation and telling primary from secondary brain tumors. Four structural sequences, two tumor masks and clinical data per patient.
Overview
MOTUM (multi-origin brain tumor MRI) is a small multi-center collection of brain MRI from patients with either a high-grade glioma or a brain metastasis whose primary cancer is known from pathology. The authors, from three Chinese hospitals together with TU Munich and Harvard Medical School, released it so that segmentation and classification methods can be tested on the hard problem of telling gliomas apart from metastases, and metastases of different origin apart from each other.
Composition
The cohort has 67 patients. 29 have a high-grade glioma (glioblastoma, grade 3 or 4 astrocytoma, or oligodendroglioma). The other 38 have brain metastases from lung (20), breast (10), ovarian (4), gastric (2) and melanoma (2) primaries. Each patient has FLAIR, T1-weighted, contrast-enhanced T1-weighted and T2-weighted images. A clinical spreadsheet gives sex, age at MRI, pathology, immunohistochemistry or molecular results and the extent of surgery. Radiomics features (110 PyRadiomics features) are included as derivatives.
Acquisition
Scans come from routine clinical care of patients diagnosed between 2019 and 2022 at the Second Affiliated Hospital of Anhui Medical University, Changzheng Hospital and the First Affiliated Hospital of USTC, on Siemens or Philips 3 T systems with 5 mm slices. Images were converted to NIfTI, skull-stripped with HD-BET and rigidly registered to the T1 image, keeping the native 2D resolution. Scans with severe motion were excluded.
Annotations
Two masks per patient: non-enhancing FLAIR abnormality and contrast-enhancing tumor on T1-ce. Thirty patients were segmented by hand in ITK-SNAP and corrected by two physicians. The remaining 37 were pre-segmented by a 2D nnU-Net trained on those 30 and then corrected by the same physicians. The trained model is shared as a Docker container.
Known limitations
The cohort is small and several metastasis groups have only two to four patients. Slices are thick (5 mm). Version 2 on Harvard Dataverse lists image folders for 66 subjects: 012-LungMeta-AYEY has masks and radiomics but no images, and the clinical spreadsheet lists 12 breast metastasis rows against 10 in the paper. The paper states adults as an inclusion criterion but also mentions consent from the parents of a minor, and the clinical data list one 15-year-old glioma patient.
Cohort
Aggregate numbers from the sources below. Bars are relative to the 67 subjects.
Contrast / sequence
Groups can overlap
- FLAIR 67 100%
- T1-weighted 67 100%
- T1-weighted, contrast enhanced 67 100%
- T2-weighted 67 100%
Background & Summary in Gong et al. 2024, A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors (Scientific Data 11, 789)
Field strength
- 3 T 67 100%
Image acquisition in Gong et al. 2024, A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors (Scientific Data 11, 789)
Condition
Groups can overlap
- Brain metastases 38 57%
- Glioma 29 43%
- Lung cancer 20 30%
- Breast cancer 10 15%
- Ovarian cancer 4 6%
- Gastric cancer 2 3%
- Melanoma 2 3%
Subject characteristics, Abstract in Gong et al. 2024, A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors (Scientific Data 11, 789)
Country
- China 67 100%
Subject characteristics in Gong et al. 2024, A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors (Scientific Data 11, 789)
Contrast combinations
How many subjects have exactly each set of contrasts.
| T1w | T1w_ce | T2w | FLAIR | Subjects with exactly this set |
|---|---|---|---|---|
67 |
Background & Summary in Gong et al. 2024, A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors (Scientific Data 11, 789)
License and access
Our reading of the license, not legal advice. Before you use the data, read the original license and confirm that your use is allowed. We take no responsibility for how you use a dataset. Full disclaimer
Download without an account
Processed NIfTI images, masks, radiomics features and the clinical spreadsheet download without an account from Harvard Dataverse. The G-Node GIN release cited in the paper (https://doi.org/10.12751/g-node.tvzqc5) also holds the anonymized DICOM files.
Creative Commons Attribution 4.0 International
Use, share and adapt the data for any purpose, including commercial use, as long as you credit the creators.
Harvard Dataverse and the G-Node GIN record both list CC BY 4.0.
What you can do
- Yes
- Yes
- Yes
- Yes
- Yes
What you can share
- Yes
- Yes
- Yes
What you must do
- Yes
- Share alike No
- No
- No
- Manuscript review No
- Release code No
- Return results No
- Delete after use No
Limits
- No
- Location limits No
Citation
Gong Z, Xu T, Peng N, Cheng X, Niu C, Wiestler B, Hong F, Li HB. A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors. Scientific Data 11, 789 (2024). https://doi.org/10.1038/s41597-024-03634-0
Sources
Every number on this page comes from one of these documents. Each chart names the table or page it is taken from. The raw numbers are in stats.csv.