Brain Tumor Surgery With 3-Dimensional Surface Navigation
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it