Resectability of White Matter Tracts in Patients With Language-Critical Gliomas
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Achieving maximal safe resections is critical for patients with language-eloquent gliomas. White matter tracts (WMTs) may offer valuable insights for surgical decision making, but the resectability of specific tracts remains unclear. In this study, we aim to identify predictors of permanent postoperative language deficits (PLDs) using resections of WMTs, illuminating the functional relevance of specific white matter segments. METHODS: This retrospective cohort study analyzed 78 consecutive glioma patients undergoing resection of language-eloquent tumors. WMT reconstructions were performed for 6 language-associated tracts. Tracts were parcellated according to anatomic location. The positive predictive value (PPV-tract resections with PLDs divided by total tract resections) was calculated for each tract segment. Resected tract segments (RTS) were normalized to Montreal Neurological Institute space. RESULTS: WMT resection strongly predicted PLDs (odds ratio = 16.00, P = .0115), with high negative predictive value (NPV, 97.4%), but low PPV (29.6%). RTS in patients with PLDs clustered within the white matter of the temporoparietal junction and middle temporal lobe. RTS without sequela localized more diffusely, with notable concentrations in the frontal operculum, anterior temporal lobe, and orbitofrontal region. Tract segments in the frontal lobe demonstrated near-complete resectability (frontal arcuate fasciculus [AF], frontal inferior fronto-occipital fasciculus, frontal uncinate fasciculus, frontal superior longitudinal fasciculus PPV = 0). Tract segments in the parietal and temporal lobe demonstrated significantly less resectability (parietal AF PPV = 0.4; temporal AF PPV = 1; temporal inferior fronto-occipital fasciculus PPV = 0.5; temporal inferior longitudinal fasciculus PPV = 0.5). The volume of tract resected did not significantly differ according to functional outcome (U = 84.0, P = .70). Resections of parietal and temporal tract segments robustly predicted PLDs (odds ratio = 29.4, P < .001), with a notable increase in PPV (50%) compared with the generalized model. CONCLUSION: This study demonstrates that resecting WMTs is a robust predictor of PLDs. WMTs in the orbitofrontal region, anterior temporal lobe, and frontal operculum were resectable whereas tract resections in the parietal and temporal lobes significantly increased risk of PLDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".