ArticleFrontiers in oncology2023
Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated longitudinal treatment response assessment of brain tumors: A systematic review.Neuro-oncology · 2025Pooled it
- Report-Level Impact of DL Assistance on Teleradiology Quality Support for Brain Metastases: Real-World Clinical Practice at a Single Tertiary Center.Diagnostics (Basel, Switzerland) · 2026Article
- Attention-enhanced segmentation network for automated cerebral microbleed detection and burden assessment.Frontiers in neuroscience · 2026Article
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Authors and funding
9 authors.
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Abstract
Purpose/objectives: Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment. Methods and materials: A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment response. The detection sensitivity, precision, Dice similarity coefficient (DSC), and agreement of treatment response assessments between neuroradiologists and RLK-Unet were assessed. Results: RLK-Unet demonstrated a sensitivity of 86.9% and a precision of 79.6% for BMs and had a DSC of 0.663. Segmentation performance was better in the subgroup with larger BMs (DSC, 0.843). The agreement in the response assessment for BMs between the radiologists and RLK-Unet was excellent (intraclass correlation, 0.84). Conclusion: RLK-Unet yielded accurate detection and segmentation of BM and could assist clinicians in treatment response assessment.
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