Evaluation of a Navigated 3D Ultrasound Integration for Brain Tumor Surgery: First Results of an Ongoing Prospective Study
Bibliographic record
Abstract
The aim of the study was to assess the quality, accuracy and benefit of navigated 2D and 3D ultrasound for intra-axial tumor surgery in a prospective study. Patients intended for gross total resection were consecutively enrolled. Intraoperatively, a 2D and 3D iUS-based resection was performed. During surgery, the image quality, clinical benefit and navigation accuracy were recorded based on a standardized protocol using Likert’s scales. A total of 16 consecutive patients were included. Mean ratings of image quality in 2D iUS were significantly higher than in 3D iUS (p < 0.001). There was no relevant decrease in rating during the surgery in 2D and 3D iUS (p > 0.46). The benefit was rated 2.2 in 2D iUS and 2.6 in 3D iUS (p = 0.08). The benefit remained stable in 2D, while there was a slight decrease in the benefit in 3D after complete tumor resection (p = 0.09). The accuracy was similar in both (mean 2.2 p = 0.88). Seven patients had a small tumor remnant in intraoperative MRT (mean 0.98 cm3) that was not appreciated with iUS. Crucially, 3D iUS allows for an accurate intraoperative update of imaging with slightly lower image quality than 2D iUS. Our preliminary data suggest that the benefit and accuracy of 2D and 3D iUS navigation do not undergo significant variations during tumor resection.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".