ITVT-03. Use of Functional MRI and DTI for Surgical Planning of Maximal Extent of Resection of CNS Tumors - A Single Neurosurgical Center’s Experience
Bibliographic record
Abstract
Abstract Use of functional neuroimaging capabilities such as fMRI, DTI, MRP, MRS, AS-PET-CT, SPECT, and TMS as noninvasive tools to visualize intrinsic brain and spine morphology in relation to function have developed over the past 30 years. Amongst these imaging modalities, functional magnetic resonance imaging (fMRI) is of particular interest since it follows the physiological coupling between neuronal electrical activity and metabolic structural (cellular) activity as it relates to tissue vascularity and perfusion states. As an adjunct to this modality, MRI-based diffusion tensor imaging (DTI) allows further detailed radiographic assessment of fiber tracts in the brain in relationship to the surgical lesion of interest. In addition, combination of other imaging modalities including MR perfusion and intraoperative tools such as neuronavigation and direct cortical stimulation can help guide the extent of maximal safe resection. Herein we present a case series from our neurosurgical institution of primary intra-axial, extra-axial, supratentorial, and brainstem tumors resected using fMRI and DTI for presurgical planning allowing for maximal safe extent of resection and outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".