P09.10 Clinical fMRI in low grade glioma patients: impact on surgical decision making and patient outcomes
Bibliographic record
Abstract
BACKGROUND: This study aims to evaluate the impact of preoperative functional magnetic resonance imaging (fMRI) on low grade glioma (LGG) patients’ outcomes and surgical planning. METHODS: In this retrospective matched cohort study of a single surgeon’s patients, we are comparing two groups of LGG patients (WHO grade II) based on exposure to fMRI. Sixteen LGG patients who underwent fMRI were selected, and 32 control (non-fMRI exposed) patients are being selected through propensity score matching from a pool of 764 brain tumour patients. Outcomes being compared include time between clinical presentation and surgery, adverse surgical outcomes, extent of tumour resection, preoperative and postoperative functional status, and overall mortality. To assess the impact of fMRI data on clinicians’ decision making process, neurosurgeons within a single centre are completing questionnaires regarding treatment options for each LGG fMRI patient based on clinical data and structural imaging before and after fMRI. The questionnaire includes questions regarding expectations of the tumours’ eloquence, preferred treatment option, expected extent of resection, and degree of confidence that the preferred treatment option is optimal. RESULTS: Within the group of 16 LGG patients who have undergone fMRI studies over a 12-year period, mean age was 40 years, and most presented with seizures (81%). Most lesions were left-sided (81%), and the lobes most commonly involved were frontal (75%) and temporal (31%). Patients underwent either craniotomy (50%), stereotactic biopsy (25%) or nonsurgical management (25%). Nine patients had 1p/19q analysis performed, and three (33%) showed 1p/19q codeletion. Mean time between clinical presentation and fMRI was 3.3 ± 1.9 weeks. In patients who were initially managed surgically, mean time between fMRI and surgery was 3.8 ± 2.0 weeks. Surgical complications or post-operative neurological deficits were seen in four patients who underwent craniotomy (50%) and one patient who underwent biopsy (25%). All complications were mild and/or temporary. In surgical patients, between pre-operative assessment and eight week post-operative follow-up, mean modified Rankin scale improved from 1.80 ± 0.79 to 1.50 ± 0.97. In our cohort, 5-year mortality was 12.5% (mean follow-up duration 5.46 years). CONCLUSIONS: Data analysis is ongoing with plans to compare relevant demographics and outcomes of brain tumour patients based on exposure to fMRI, and to analyse questionnaires to elucidate how surgeons incorporate fMRI data into their therapeutic approach.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".