Multi-modality imaging assisted fluorescence-guided resection of glioblastoma: Case report
Bibliographic record
Abstract
Glioblastoma is a highly malignant and infiltrative brain tumor, with a median overall survival of about 15 months. Gross-total resection using 5-aminolevulinic acid (5-ALA) assisted fluorescence-guided tumor resection has been shown to prolong progression free survival. Here, we report the utility of multi-modality imaging in conjunction with the 5-ALA fluorescence in resection of an IDH (R132H) wildtype malignant astrocytoma. A 58-year old male, presented with a generalized seizure and was found to have a right-anterior temporal lobe lesion, measuring 7.42 cm3 in volume. Given the patient's left-hand dominance, functional-MRI and white-matter tractography using diffuse tensor imaging was performed. These image series, along with T1-weighted contrast enhanced MRI and CT scans were inter-registered and fused to create a multi-modality image dataset. This fused dataset was used in preoperative planning and intraoperatively for stereotactic surgical navigation. A gross-total resection of the tumor was achieved for this case. Three other glioblastoma cases were performed at this site using the same technique described. The average extent of resection achieved was 96 ± 4%, with no post-operative neurological complications. While it is not clear that 5-ALA fluorescence guided resection alone improves the overall survival of patients with glioblastoma, this intra-operative adjunct certainly enables complete resections of contrast-enhancing tumors, leading to improved progression-free survival. This case study shows a single-institution experience with multi-modality fluoresce-guided tumor resection – providing the surgeon with the safest avenue to aggressively excise tumor with a goal to achieve maximal resection with greater efficacy and safety.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".