ArticleNeuro-oncology advances
Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Effect of Anatomic Contextual Information on the Performance of a Convolutional Neural Network Tasked With Brain Metastasis Detection.Advances in radiation oncology · 2026Article
- Association of recurrence patterns and outcome with HR and HER2 status in patients with resected brain metastases from breast cancer.International journal of cancer · 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
- A 3D Convolutional Neural Network Based on Non-enhanced Brain CT to Identify Patients with Brain Metastases.Journal of imaging informatics in medicine · 2025Article
- Principles of artificial intelligence in radiooncology.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.European radiology experimental · 2025Article
- Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Development and validation of a deep learning model using MR imaging for predicting brain metastases: an accuracy-focused study.Frontiers in oncology · 2025Article
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- Article
- Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.Neuro-oncology · 2024Article
- External evaluation of a deep learning-based approach for automated brain volumetry in patients with huntington's disease.Scientific reports · 2024Article
- Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging.Mayo Clinic proceedings. Digital health · 2024Article
- A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information.Scientific data · 2024Article
- Prognostic value of MRI volumetric parameters in non-small cell lung cancer patients after immune checkpoint inhibitor therapy: comparison with response assessment criteria.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis.Cancers · 2023Review
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM. Methods: A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity). Results: The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001-0.2 cm Conclusion: The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.
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