Identifying preoperative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas
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Bibliographic record
Abstract
OBJECT Diffusion MRI has uniquely enabled in vivo delineation of white matter tracts, which has been applied to the segmentation of eloquent pathways for intraoperative mapping. The last decade has also seen the development from earlier diffusion tensor models to higher-order models, which take advantage of high angular resolution diffusion-weighted imaging (HARDI) techniques. However, these advanced methods have not been widely implemented for routine preoperative and intraoperative mapping. The authors report on the application of residual bootstrap q-ball fiber tracking for routine mapping of potentially functional language pathways, the development of a system for rating tract injury to evaluate the impact on clinically assessed language function, and initial results predicting long-term language deficits following glioma resection. METHODS The authors have developed methods for the segmentation of 8 putative language pathways including dorsal phonological pathways and ventral semantic streams using residual bootstrap q-ball fiber tracking. Furthermore, they have implemented clinically feasible preoperative acquisition and processing of HARDI data to delineate these pathways for neurosurgical application. They have also developed a rating scale based on the altered fiber tract density to estimate the degree of pathway injury, applying these ratings to a subset of 35 patients with pre- and postoperative fiber tracking. The relationships between specific pathways and clinical language deficits were assessed to determine which pathways are predictive of long-term language deficits following surgery. RESULTS This tracking methodology has been routinely implemented for preoperative mapping in patients with brain gliomas who have undergone awake brain tumor resection at the University of California, San Francisco (more than 300 patients to date). In this particular study the authors investigated the white matter structure status and language correlation in a subcohort of 35 subjects both pre- and postsurgery. The rating scales developed for fiber pathway damage were found to be highly reproducible and provided significant correlations with language performance. Preservation of the left arcuate fasciculus (AF) and the temporoparietal component of the superior longitudinal fasciculus (SLF-tp) was consistent in all patients without language deficits (p < 0.001) at the long-term follow-up. Furthermore, in patients with short-term language deficits, the AF and/or SLF-tp were affected, and damage to these 2 pathways was predictive of a long-term language deficit (p = 0.005). CONCLUSIONS The authors demonstrated the successful application of q-ball tracking in presurgical planning for language pathways in brain tumor patients and in assessing white matter tract integrity postoperatively to predict long-term language dysfunction. These initial results predicting long-term language deficits following tumor resection indicate that postoperative injury to dorsal language pathways may be prognostic for long-term clinical language deficits. Study results suggest the importance of dorsal stream tract preservation to reduce language deficits in patients undergoing glioma resection, as well as the potential prognostic value of assessing postoperative injury to dorsal language pathways to predict long-term clinical language deficits.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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