ArticleScientific data2024
A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information.
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 20 papers.
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
20 citing papers in PubMed.
- Automatic Segmentation and Classification of Glioblastoma and Solitary Brain Metastasis Using a Deep Learning Model on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Diagnosis of Multiple Sclerosis Using Multimodal Deep Learning Integrating Lesion and Normal-Appearing White Matter: A Retrospective Study with International Multicentre External Validation.medRxiv : the preprint server for health sciences · 2026Article
- Brain metastases converge on shared geometric architecture and transcriptomic landscape yet remain distinct from gliomas.iScience · 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
- Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases.Frontiers in oncology · 2026Article
- Evaluating the detection of small brain lesions in magnetic resonance using deep learning.Frontiers in neuroscience · 2026Article
- Towards user-centered interactive medical image segmentation in VR with an assistive AI agent.Virtual reality · 2026Article
- An Integrated Dataset of Metastatic Breast Cancer to the Brain with Imaging, Radiomics, and Tumor Genetics.Scientific data · 2025Article
- A longitudinal MRI dataset of brain metastases with tumor segmentations, clinical & radiomic data.Scientific data · 2025Article
- Artificial Intelligence-Based MRI Segmentation for the Differential Diagnosis of Single Brain Metastasis and Glioblastoma.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs).Brain sciences · 2025Review
- Characteristics, licensing, and ethical considerations of openly accessible oral-maxillofacial imaging datasets: a systematic review.NPJ digital medicine · 2025Article
- Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.Journal of magnetic resonance imaging : JMRI · 2025Article
- Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting Survival Rates in Brain Metastases Patients from Non-Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.European radiology experimental · 2025Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Article
- Morphological MRI features as prognostic indicators in brain metastases.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information.Scientific data · 2024Article
Corrections and comments
- Update of
Authors and funding
22 authors.
Funding
Abstract
Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.
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.