ArticleBMC medical imaging2025
Histogram analysis of IVIM MRI for differentiating glioma IDH-1 status, grade, and Ki-67 expression: a comparison of bi-exponential models at multiple B-value.
Article in BMC medical imaging, 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
10 authors.
Funding
Abstract
backgroundIntravoxel Incoherent Motion (IVIM) offers valuable insights into tumor microstructural heterogeneity through diffusion and perfusion. However, the relationship between these features and glioma grading, IDH-1 status, and Ki-67 expression remains insufficiently explored, and there is no consensus on the optimal B-value range for IVIM imaging, which may impact the consistency of results.
methodsWe retrospectively analyzed IVIM data from 73 glioma patients (mean age, 52.83 years ± 12.79 [SD]; 40 male). Histogram features of IVIM parameters, calculated with four B-value ranges (0-800, 0-1200, 0-1500, and 0–2000 s/mm²), were extracted from the tumor parenchyma. These features were compared across different IDH-1 status, tumor grade, and Ki-67 proliferation index. After checking for multicollinearity, parameters with significant differences were further analyzed using logistic regression, receiver operating characteristic analysis, and decision curve analysis to compare their performance. And Bonferroni correction was applied to adjust p-values for multiple comparisons.
resultsIndependent risk factors of IDH-1 status, tumor grade and Ki-67 level were identified through multivariate logistic regression in B800, B1200, B1500 and B2000 model. ROC analysis revealed that all models showed similar AUCs for IDH-1 status, tumor grade, and Ki-67 level prediction. The B800 model had the highest specificity (0.87) for IDH-1 status prediction, the B1500 model had the highest specificity (0.91) for tumor grade prediction, and the B1200 model exhibited the highest sensitivity (0.94) for Ki-67 level prediction. The B1500 and B 2000 model demonstrates the best net benefit in most threshold ranges for IDH-1 status, tumor grade and Ki-67 level identification.
conclusionHistogram features of IVIM parameters demonstrated well performance in distinguishing glioma characteristics and the maximum 1500 s/mm2 may be the optimal.
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.