Trial reportScientific reports2018
Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors.
Trial report in Scientific reports, 2018. 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.
What it found
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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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain metastasis detection using machine learning: a systematic review and meta-analysis.Neuro-oncology · 2021Pooled it
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: a clinical cohort study.European radiology · 2024Article
- SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS.Magnetic resonance in medicine · 2023Article
- Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.European radiology · 2023Article
- Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment.Journal of digital imaging · 2022Article
- Article
- Artificial Intelligence in Neuro-Oncologic Imaging: A Brief Review for Clinical Use Cases and Future Perspectives.Brain tumor research and treatment · 2022Review
- Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging.European radiology · 2021Article
- Contrast-Enhanced Black Blood MRI Sequence Is Superior to Conventional T1 Sequence in Automated Detection of Brain Metastases by Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2021Article
- [The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging].Taehan Yongsang Uihakhoe chi · 2020Review
- Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging.Korean journal of radiology · 2020Review
- [Current Applications and Future Perspectives of Brain Tumor Imaging].Taehan Yongsang Uihakhoe chi · 2020Review
- Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties.Molecules (Basel, Switzerland) · 2019Article
- Deep Neural Network Inverse Design of Integrated Photonic Power Splitters.Scientific reports · 2019Article
- Application of Radiomics for Personalized Treatment of Cancer Patients.Cancer management and research · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Black-blood (BB) imaging is used to complement contrast-enhanced 3D gradient-echo (CE 3D-GRE) imaging for detecting brain metastases, requiring additional scan time. In this study, we proposed deep-learned 3D BB imaging with an auto-labelling technique and 3D convolutional neural networks for brain metastases detection without additional BB scan. Patients were randomly selected for training (29 sets) and testing (36 sets). Two neuroradiologists independently evaluated deep-learned and original BB images, assessing the degree of blood vessel suppression and lesion conspicuity. Vessel signals were effectively suppressed in all patients. The figure of merits, which indicate the diagnostic performance of radiologists, were 0.9708 with deep-learned BB and 0.9437 with original BB imaging, suggesting that the deep-learned BB imaging is highly comparable to the original BB imaging (difference was not significant; p = 0.2142). In per patient analysis, sensitivities were 100% for both deep-learned and original BB imaging; however, the original BB imaging indicated false positive results for two patients. In per lesion analysis, sensitivities were 90.3% for deep-learned and 100% for original BB images. There were eight false positive lesions on the original BB imaging but only one on the deep-learned BB imaging. Deep-learned 3D BB imaging can be effective for brain metastases detection.
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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.