ArticleBMC neurology2025
Preoperative prediction of brain invasion in meningiomas: a comparison of diffusion kurtosis imaging and diffusion tensor imaging.
Article in BMC neurology, 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
12 authors.
Funding
Abstract
objectiveIdentifying brain invasion particularly important for meningioma patients, but there is still a lack of valuable early biomarkers. The authors aimed to investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in predicting the brain invasion of meningioma.
methodsA total of 132 meningioma patients were prospectively included and underwent magnetic resonance diffusion imaging. The whole-tumour histogram parameters were extracted from diffusion maps including Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD). The Mann-Whitney U test was used to compare the parameters of brain invasive and non-invasive meningiomas. The receiver operating characteristic (ROC) curve and multiple logistic regression analyes were performed to identify the diagnostic performance. Spearman's partial correlation was used to evaluate correlations between histogram parameters and the Ki-67 index.
resultsDKI-MK (10th, 50th, 90th percentile, maximum, mean and kurtosis), DKI-FA (minimum, maximum), DKI-MD (minimum, maximum, kurtosis and skewness), DTI-FA (maximum), DTI-MD (10th percentile, kurtosis and skewness) showed statistically significant differences between brain invasive and non-invasive meningiomas (p < 0.05). For all histogram parameters, the highest individual predictor was DKI-MK 90th percentile with an AUC of 0.837 and an accuracy of 75.0%. The DKI combined model can further improve the diagnostic efficiency, with an AUC of 0.914 and an accuracy of 85.6%. Significant correlations were found between various diffusion histogram parameters and the Ki-67 index (rho = -0.244-0.504, p < 0.05).
conclusionsThe whole-tumour DKI and DTI histogram analysis is a promising approach for predicting brain invasion in meningiomas, and the multi-parameter combined model can further improve diagnostic efficiency.
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.