Brain Tumor Surgery With 3-Dimensional Surface Navigation
Bibliographic record
Abstract
BACKGROUND: Precise lesion localization is necessary for neurosurgical procedures not only during the operative approach, but also during the preoperative planning phase. OBJECTIVE: To evaluate the advantages of 3-dimensional (3-D) brain surface visualization over conventional 2-dimensional (2-D) magnetic resonance images for surgical planning and intraoperative guidance in brain tumor surgery. METHODS: Preoperative 3-D brain surface visualization was performed with neurosurgical planning software in 77 cases (58 gliomas, 7 cavernomas, 6 meningiomas, and 6 metastasis). Direct intraoperative navigation on the 3-D brain surface was additionally performed in the last 20 cases with a neurosurgical navigation system. For brain surface reconstruction, patient-specific anatomy was obtained from MR imaging and brain volume was extracted with skull stripping or watershed algorithms, respectively. Three-dimensional visualization was performed by direct volume rendering in both systems. To assess the value of 3-D brain surface visualization for topographic lesion localization, a multiple-choice test was developed. To assess accuracy and reliability of 3-D brain surface visualization for intraoperative orientation, we topographically correlated superficial vessels and gyral anatomy on 3-D brain models with intraoperative images. RESULTS: The rate of correct lesion localization with 3-D was significantly higher (P = .001, χ), while being significantly less time consuming (P < .001, χ) compared with 2-D images. Intraoperatively, visual correlation was found between the 3-D images, superficial vessels, and gyral anatomy. CONCLUSION: The proposed method of 3-D brain surface visualization is fast, clinically reliable for preoperative anatomic lesion localization and patient-specific planning, and, together with navigation, improves intraoperative orientation in brain tumor surgery and is relatively independent of brain shift.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".