Comparative Evaluation of Lower Gadolinium Doses for MR Imaging of Meningiomas: How Low Can We Go?
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND PURPOSE: Gadolinium-based contrast agents are widely used for meningioma imaging; however, concerns exist regarding their side effects, cost, and environmental impact. At the standard gadolinium dose, most meningiomas show avid contrast enhancement suggesting that administering a smaller dose may be feasible. The purpose of this study was to evaluate the impact of a lower gadolinium dose on the differentiation between meningiomas and adjacent intracranial tissues. MATERIALS AND METHODS: 108 patients with presumed or confirmed meningiomas who underwent brain MRI at multiple doses of gadolinium were included in the study. The patients9 MRIs were categorized into three groups based on the gadolinium dose administered: Micro (approximately 25% of the standard dose), Low (approximately 62% of the standard dose) and Standard dose. Multi-reader qualitative visual assessment and quantitative relative signal differences calculations were performed to evaluate tumor differentiation from the cortex and from the dural venous sinus. The relative signal differences for each dose were analyzed using ANOVA for quantitative assessment and NcNemar for qualitative assessment. Additionally, non-inferiority testing was used to compare the Low and Micro doses to the Standard dose. RESULTS: Decreasing the gadolinium dose to a Low dose or a Micro dose resulted in a statistically significant decrease in signal difference between the tumor and the adjacent brain tissues (p<0.02). However, on visual assessment, the Low dose was non-inferior to the Standard dose. The proportion of cases with suboptimal differentiation was significantly higher for the Micro dose than for the Standard dose, both for the differentiation between the tumor and the cortex (p=0.041) and the differentiation between tumor and sinus (p<0.001). CONCLUSIONS: Reducing the gadolinium dose to 62% of the standard level still allows for sufficient visual delineation of meningiomas from surrounding tissues. However, further reduction to 25% substantially compromises the ability to distinguish the tumor from adjacent structures and is, therefore, not advisable. ABBREVIATIONS: GBCAs = Gadolinium-based contrast agents; SSS = Superior Sagittal Sinus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".