MRI based volumetric measurements of vestibular schwannomas in patients with neurofibromatosis type 2: comparison of three different software tools
Bibliographic record
Abstract
Neurofibromatosis type 2 is a neurogenetic disorder with an incidence of about 1:33.000. Hallmarks are bilateral benign vestibular schwannomas, which can lead to deafness or brainstem compression. Volumetric tumor measurements are essential to assess the efficacy of new therapies. We present a statistical and methodical comparison of three volumetric image analysis tools. We performed volumetric measurements on phantoms with predefined volumes (0.1 to 8.0 ml) and tumors seen on 32 head MRI scans from eight NF2 patients with BrainLab, ITK-Snap, or OsiriX. The software was compared with regard to accuracy and reproducibility of the measurements and time required for analysis. The mean volume estimated by all three software programs differed significantly from the true volume of the phantoms, but OsiriX and BrainLab gave estimates that were not significantly different from each other. For the actual tumors, the estimated volumes with all three software tools showed a low coefficient of variability, but the mean volume estimates differed among the tools. OsiriX showed the shortest analysis time. Volumetric assessment of MRI images is associated to an intrinsic risk of miscalculation. For precise volumes it is mandatory to use the same volumetric tools for all measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".