fMRI-Driven DTT Assessment of Corticospinal Tracts Prior to Cortex Resection
Bibliographic record
Abstract
BACKGROUND: The role of diffusion tensor tractography (DTT) has become increasingly important in the preoperative mapping of brain white matter. Recently, functional magnetic resonance imaging (fMRI) driven DTT has provided the ability to evaluate the spatial relationship between the corticospinal tract (CST) and motor resection tumor boundaries. The main objective of this study was improvement of the preoperative assessment of the CST in patients with gliomas involving the motor cortical areas. METHODS: Seventeen patients with gliomas involving motor cortical areas underwent 3 dimensions (3D) T1-weighted imaging for anatomical referencing, using both fMRI and diffusion tensor imaging (DTI). We used the fast-marching tractography (FMT) algorithm to define the 3D connectivity maps within the whole brain using seed points selected in the white matter adjacent to the location of fMRI activation. The target region of interest (ROI) was placed in the cerebral peduncle. Karnofsky performance status (KPS) scores were evaluated for each patient before and after surgery. RESULTS: The CST of a total seventeen patients were successfully tracked by choosing seed and target ROI on the path of the fibers. What is more, DTT can indicate preoperatively the possibility for total glioma removal or the maximum extent of surgical resection. The postoperative average KPS score for the seventeen patients enrolled increased by more than 10 points. CONCLUSIONS: Incorporation of fMRI driven DTT showed a maximum benefit in surgical treatment of gliomas. Our study of the assessment precision should enhance the accuracy of glioma operations with a resulting improvement in postoperative patient outcome.
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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) | 0.000 | 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) | 0.001 | 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".