Quantitative susceptibility mapping study of deep gray matter iron content in glioma patients
Bibliographic record
Abstract
Background: Iron is associated with the pathophysiology of gliomas. The measurement of brain iron levels plays a vital role in defining the pathophysiologic changes caused by gliomas. This study aimed to analyze the effect of gliomas with different histopathology and molecular characteristics on deep gray matter (DGM) iron content and the relationship between DGM iron content and cognition in glioma patients (PT). Methods: In this retrospective study, we included 81 PT, categorized according to different histopathology and molecular characteristics, and 30 age- and gender-matched healthy controls (HC). Brain quantitative susceptibility mapping (QSM) maps were computed from three-dimensional (3D) multi-echo gradient-echo data using Laplacian-based phase unwrapping, a variational sharpening (V-SHARP) background field correction and the streaking artifacts reduction (STAR)-QSM method. ITK-SNAP was used to measure the susceptibility values reflecting the iron content in the regions of interest. Differences in DGM magnetic susceptibility between groups were compared. Pearson's correlation analysis assessed the relationship between DGM magnetic susceptibility and Montreal Cognitive Assessment (MoCA). Results: 24.70±2.20, P=0.021). There was a negative correlation between MoCA scores and DGM magnetic susceptibility. Conclusions: The DGM iron content, as measured by QSM, was higher in PT than in HC and correlated with tumor grade. Cognitive decline is associated with increased DGM iron content in PT. The DGM iron content could be a potential biomarker in glioma differentiation and prognosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".