ArticleBrain and behavior2026
Assessment of pTERT Subtypes of Glioblastoma by Quantitative FLAIR Analysis Based on Fusing MRI Images.
Article in Brain and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Assessment of pTERT Subtypes of Glioblastoma by Quantitative FLAIR Analysis Based on Fusing MRI Images.Brain and behavior · 2026Article
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4 authors.
Funding
Abstract
backgroundMutations in the telomerase reverse transcriptase promoter (pTERT) are important molecular markers in glioblastoma (GBM). Although several imaging-based approaches have attempted to predict pTERT mutation status preoperatively, the value of quantitative metrics extracted from lesion subregions remains unclear. This study investigated whether quantitative FLAIR metrics derived from contrast-enhanced T1-weighted imaging (CE T1WI)-FLAIR fused images contribute to the differentiation of pTERT subtypes in GBM.
methodsMRI and clinical data from 135 GBM patients, 94 with pTERT-mutant (pTERTm) and 41 with pTERT-wild-type (pTERTw) tumors, were retrospectively analyzed. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Clinical characteristics and conventional MRI variables were compared between pTERTm and pTERTw groups. FLAIR signal intensity (SI) metrics were measured in three subregions on CE T1WI-FLAIR fused images: the enhancement region, the edema region (non-enhancing), and the whole lesion (enhancement + edema). Significant variables identified by logistic regression were incorporated into clinical, MRI, and combined predictive models. Model performance was internally evaluated using leave-one-out cross-validation (LOOCV) in the training cohort and externally assessed in the validation cohort.
resultsSignificant differences between pTERTm and pTERTw groups were observed in age, FLAIR SI standard deviation (FLAIR
conclusionsQuantitative FLAIR metrics extracted from subregions on CE T1WI-FLAIR fused images differ significantly between pTERTm and pTERTw GBM. Subregional quantitative analysis may therefore contribute to noninvasive preoperative prediction of pTERT mutation status.
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