ArticleFrontiers in neuroscience2025
Automated brain atrophy quantification from clinical MRI predicts early neurological deterioration in anterior choroidal artery territory infarction.
Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Early neurological deterioration (END) occurs in 43%-60% of patients with anterior choroidal artery (AChA) territory infarction. While brain atrophy serves as an imaging biomarker of diminished brain reserve capacity and may influence stroke outcomes, its predictive value for END in AChA infarction remains unclear. Methods: This dual-center retrospective cohort study consecutively enrolled patients with acute AChA territory infarction admitted to two Chinese stroke centers between September 2018 and September 2024. Clinical T1-weighted images were reconstructed into standardized high-resolution images using the SynthSR deep learning algorithm, followed by fully automated brain tissue segmentation via AssemblyNet. We calculated gray matter fraction (GMF), white matter fraction (WMF), brain parenchymal fraction (BPF), and cerebrospinal fluid fraction (CSFF) to quantify brain atrophy severity. Multivariable logistic regression and restricted cubic spline (RCS) analyses were employed to assess associations between brain atrophy metrics and END. Results: Among 206 enrolled patients, 78 (37.86%) developed END. Patients with END demonstrated significantly greater brain atrophy: GMF ( Conclusion: Automated brain atrophy quantification based on routine clinical MRI can independently predict END risk in patients with AChA infarction, providing a feasible imaging biomarker for this high-risk stroke population and facilitating early risk stratification and treatment optimization.
Indexed as
Identifiers
What OpenQuestion holds
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.