Temporal Lobectomy for High Grade Gliomas: Impact on Outcomes and Implications for Postoperative Radiation Treatment Field Design
Bibliographic record
Abstract
Objective: To review clinical outcomes for patients with high grade gliomas (WHO Grades 3 and 4) who have undergone temporal lobectomy or wide local excision (WLE) with particular attention to rates of recurrence within temporal lobectomy resection bed, and to assess the potential impact of this surgical approach on the design of postoperative radiotherapy treatment volumes. Method: We reviewed outcomes for 17 patients with diagnosis of high grade glioma located in temporal lobe who underwent either wide local excision (WLE, 18 procedures in 13 patients) or formal temporal lobectomy (5 procedures in 4 patients). Location of recurrence was identified for each, and classified as involving or not involving temporal lobectomy resection cavity. STANDARD and MODIFIED (temporal lobectomy resection cavity subtracted from CTV46Gy and CTV60Gy) treatment plans were generated for each patient with intensity modified radiotherapy (IMRT). Dosimetric data were collected for each plan and compared. Results: Only 1/5 of patients suffered recurrence following temporal lobectomy (none in temporal lobectomy resection cavity) with median follow up 27.3 months. 16/17 (94.1%) of recurrences after WLE involved temporal lobe). MODIFIED IMRT plans reduced dose to all critical organs versus while providing equivalent PTV coverage. Conclusions: Following temporal lobectomy, we identified no recurrences within the temporal lobectomy resection cavity, suggesting that this region might safely be excluded from postoperative radiotherapy planning treatment volumes. With IMRT, this improves ability to spare critical normal structures.
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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 | 0.003 |
| 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".