Trial reportEuropean radiology2023
MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.
Trial report in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence for brain tumor segmentation, treatment planning, and outcome prediction: a comprehensive systematic review and meta-analysis.Journal of neuro-oncology · 2025Pooled it
- AI-Assisted Brain Tumor MRI Reporting and Treatment-Planning Segmentation: A Retrospective Paired Workflow Evaluation.Biomedicines · 2026Article
- Developing a segmentation cascade deep learning network based on automated prompts.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning-based image enhancement for improved black blood imaging in brain metastasis.European radiology · 2026Article
- Evaluation of deep learning-based methods for automatic detection and segmentation of brain metastases in T1-contrast MRI for stereotactic radiosurgery.Journal of applied clinical medical physics · 2026Article
- Deep learning-driven brain tumor classification and segmentation using non-contrast MRI.Scientific reports · 2025Article
- Unseen Aggressor: A Case of Amelanotic Melanoma With Orbital Metastasis and Imaging-Pathology Correlation.Cureus · 2025Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Article
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- Research progress in artificial intelligence for brain metastases.Frontiers in medicine · 2025Review
- Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information.Radiology. Artificial intelligence · 2024Article
- Compressed Sensitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time.AJNR. American journal of neuroradiology · 2024Article
- Advances in determining the gross tumor target volume for radiotherapy of brain metastases.Frontiers in oncology · 2024Review
- A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.Journal of translational medicine · 2023Article
- Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model.Cancers · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivesTo develop and validate a two-stage deep learning model for automatic detection and segmentation of brain metastases (BMs) in MRI images.
methodsIn this retrospective study, T1-weighted (T1) and T1-weighted contrast-enhanced (T1ce) MRI images of 649 patients who underwent radiotherapy from August 2019 to January 2022 were included. A total of 5163 metastases were manually annotated by neuroradiologists. A two-stage deep learning model was developed for automatic detection and segmentation of BMs, which consisted of a lightweight segmentation network for generating metastases proposals and a multi-scale classification network for false-positive suppression. Its performance was evaluated by sensitivity, precision, F1-score, dice, and relative volume difference (RVD).
resultsSix hundred forty-nine patients were randomly divided into training (n = 295), validation (n = 99), and testing (n = 255) sets. The proposed two-stage model achieved a sensitivity of 90% (1463/1632) and a precision of 56% (1463/2629) on the testing set, outperforming one-stage methods based on a single-shot detector, 3D U-Net, and nnU-Net, whose sensitivities were 78% (1276/1632), 79% (1290/1632), and 87% (1426/1632), and the precisions were 40% (1276/3222), 51% (1290/2507), and 53% (1426/2688), respectively. Particularly for BMs smaller than 5 mm, the proposed model achieved a sensitivity of 66% (116/177), far superior to one-stage models (21% (37/177), 36% (64/177), and 53% (93/177)). Furthermore, it also achieved high segmentation performance with an average dice of 81% and an average RVD of 20%.
conclusionA two-stage deep learning model can detect and segment BMs with high sensitivity and low volume error. KEY POINTS: • A two-stage deep learning model based on triple-channel MRI images identified brain metastases with 90% sensitivity and 56% precision. • For brain metastases smaller than 5 mm, the proposed two-stage model achieved 66% sensitivity and 22% precision. • For segmentation of brain metastases, the proposed two-stage model achieved a dice of 81% and a relative volume difference (RVD) of 20%.
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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.