Navigated Intraoperative 2-Dimensional Ultrasound in High-Grade Glioma Surgery: Impact on Extent of Resection and Patient Outcome
Bibliographic record
Abstract
BACKGROUND: Maximizing extent of resection (EOR) and reducing residual tumor volume (RTV) while preserving neurological functions is the main goal in the surgical treatment of gliomas. Navigated intraoperative ultrasound (N-ioUS) combining the advantages of ultrasound and conventional neuronavigation (NN) allows for overcoming the limitations of the latter. OBJECTIVE: To evaluate the impact of real-time NN combining ioUS and preoperative magnetic resonance imaging (MRI) on maximizing EOR in glioma surgery compared to standard NN. METHODS: We retrospectively reviewed a series of 60 cases operated on for supratentorial gliomas: 31 operated under the guidance of N-ioUS and 29 resected with standard NN. Age, location of the tumor, pre- and postoperative Karnofsky Performance Status (KPS), EOR, RTV, and, if any, postoperative complications were evaluated. RESULTS: The rate of gross total resection (GTR) in NN group was 44.8% vs 61.2% in N-ioUS group. The rate of RTV > 1 cm3 for glioblastomas was significantly lower for the N-ioUS group (P < .01). In 13/31 (42%), RTV was detected at the end of surgery with N-ioUS. In 8 of 13 cases, (25.8% of the cohort) surgeons continued with the operation until complete resection. Specificity was greater in N-ioUS (42% vs 31%) and negative predictive value (73% vs 54%). At discharge, the difference between pre- and postoperative KPS was significantly higher for the N-ioUS (P < .01). CONCLUSION: The use of an N-ioUS-based real-time has been beneficial for resection in noneloquent high-grade glioma in terms of both EOR and neurological outcome, compared to standard NN. N-ioUS has proven usefulness in detecting RTV > 1 cm3.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".