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References from Comprehensive performance testing and external validation of an AI algorithm to detect and segment brain metastases. Local targets link to admitted publications; unresolved targets remain external evidence.
Multicenter, phase 3 randomized trial of stereotactic radiation vs. hippocampal avoidance whole brain radiation in patients with 5-20 brain metastases: recurrence, salvage therapy, and mortality endpoints
10.1016/j.ijrobp.2025.06.1010 · 2025 · External reference
Treatment for brain metastases with stereotactic radiation vs hippocampal-avoidance whole brain radiation: a randomized clinical trial
10.1001/jama.2026.0076 · 2026 · External reference
Stereotactic radiosurgery versus whole-brain radiation therapy for patients with 4-15 brain metastases: a phase III randomized controlled trial
10.1016/j.ijrobp.2020.07.2108 · 2020 · External reference
Importance of 3D-TSE imaging for patients with brain metastasis treated with SRS alone
10.1016/j.radonc.2025.111172 · 2025 · External reference
Dedicated isotropic 3-D T1 SPACE sequence imaging for radiosurgery planning improves brain metastases detection and reduces the risk of intracranial relapse
10.1016/j.radonc.2022.05.029 · 2022 · External reference
Efficacy of 3D-TSE sequence-based radiosurgery in prolonging time to distant intracranial failure: a session-wise analysis in a histology-diverse patient cohort
10.1093/neuonc/noae232 · 2025 · External reference
Deep learning–based detection and segmentation-assisted management of brain metastases
10.1093/neuonc/noz234 · 2020 · External reference
An approach for computer-aided detection of brain metastases in post-Gd T1-W MRI
10.1016/j.mri.2012.02.024 · 2012 · External reference
Computer-aided detection of brain metastasis on 3D MR imaging: observer performance study
10.1371/journal.pone.0178265 · 2017 · External reference
Additional MR contrast dosage for radiologists’ diagnostic performance in detecting brain metastases: a systematic observer study at 3 T
10.1007/s11604-014-0342-9 · 2014 · External reference
Multi-institutional study of the variability in target delineation for six targets commonly treated with radiosurgery
10.1080/0284186x.2018.1473636 · 2018 · External reference
Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks
10.1093/neuonc/noab071 · 2021 · External reference
Automated segmentation of brain metastases with deep learning: a multi-center, randomized crossover, multi-reader evaluation study
10.1093/neuonc/noae113 · 2024 · External reference
Evaluating the efficacy of a federated AI algorithm in assisting in the detection and segmentation of brain metastases: limitations and opportunities
10.1007/s11060-025-05294-5 · 2025 · External reference
The university of California San Francisco brain metastases stereotactic radiosurgery (UCSF-BMSR) MRI dataset
10.1148/ryai.230126 · 2024 · External reference
NS-HGlio: a generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics
10.1093/noajnl/vdac184 · 2023 · External reference
Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials
10.1093/neuonc/nov095 · 2015 · External reference
A data-centric approach to deep learning for brain metastasis analysis at MRI
10.1148/radiol.242416 · 2025 · External reference
Metrics reloaded: recommendations for image analysis validation
10.1038/s41592-023-02151-z · 2024 · External reference
Comprehensive evaluation of a deep learning model for automatic organs-at-risk segmentation on heterogeneous computed tomography images for abdominal radiation therapy
10.1016/j.ijrobp.2023.05.034 · 2023 · External reference
Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data
10.1186/s13014-020-01514-6 · 2020 · External reference
MetNet: computer-aided segmentation of brain metastases in post-contrast T1-weighted magnetic resonance imaging
10.1016/j.radonc.2020.09.016 · 2020 · External reference
Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network
10.1016/j.compbiomed.2018.02.004 · 2018 · External reference
Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study
10.1038/s41746-021-00398-4 · 2021 · External reference
Oncological value of MRI in brain metastasis: exploring the potential of combining post-contrast T1 TSE (SPACE) and T1 GRE (MPRAGE) for stereotactic radiosurgery planning and surveillance
10.1007/s11060-025-05365-7 · 2025 · External reference
Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging
10.1007/s00330-021-07783-3 · 2021 · External reference
Contrast-enhanced black blood MRI sequence is superior to conventional T1 sequence in automated detection of brain metastases by convolutional neural networks
10.3390/diagnostics11061016 · 2021 · External reference
Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI
10.1186/s41747-025-00554-5 · 2025 · External reference
A deep learning-based computer aided detection (CAD) system for difficult-to-detect brain metastases
10.1016/j.ijrobp.2022.09.068 · 2023 · External reference
