ArticleScientific data2023
A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Brain metastases from breast cancer using magnetic resonance imaging: A systematic review.Journal of medical radiation sciences · 2024Pooled it
- Identifying Prognostic Factors in Brain Metastasis Patients Using MRI Morphological Features: A Machine Learning and Survival Analysis Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Central Nervous System Involvement in Acute Myeloid Leukemia: From Pathophysiology to Neuroradiologic Features and the Emerging Role of Artificial Intelligence.Journal of clinical medicine · 2026Review
- RN-DFrontiers in artificial intelligence · 2026Article
- A large scale multi-dataset investigation of brain metastases distribution based on primary cancer type.BMC cancer · 2025Article
- A longitudinal MRI dataset of brain metastases with tumor segmentations, clinical & radiomic data.Scientific data · 2025Article
- Optimizing First-Line Therapeutics in Non-Small Cell Lung Cancer: Insights From Joint Modeling and Large-Scale Data Analysis.CPT: pharmacometrics & systems pharmacology · 2025Article
- Improving personalized federated learning to optimize site-specific performance in computer-aided detection/diagnosis.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Adaptive RAG-Assisted MRI Platform (ARAMP) for Brain Metastasis Detection and Reporting: A Retrospective Evaluation Using Post-Contrast T1-Weighted Imaging.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.BMC medical imaging · 2025Article
- Spatial-demographic analysis model for brain metastases distribution.La Radiologia medica · 2025Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Article
- Investigation of distributed learning for automated lesion detection in head MR images.Radiological physics and technology · 2024Article
- Morphological MRI features as prognostic indicators in brain metastases.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT).Journal of imaging informatics in medicine · 2024Article
- A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors.Scientific data · 2024Article
- Automated segmentation of ablated lesions using deep convolutional neural networks: A basis for response assessment following laser interstitial thermal therapy.Neuro-oncology · 2024Article
- Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.Journal of neuro-oncology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.