RARE-37. SURGICAL MANAGEMENT OF INCIDENTAL DIFFUSELY INFILTRATING GLIOMAS: A SINGLE INSTITUTIONAL EXPERIENCE
Bibliographic record
Abstract
Uncommonly, diffusely infiltrating gliomas (DIGs) are identified incidentally while asymptomatic. Early surgical management of these tumors has been suggested to improve outcome. We set out to identify and review the characteristics and outcomes of patients with incidental, asymptomatic DIGs. All DIGs surgically treated by the lead author between 2012 and 2016 were analyzed in order to identify cases that were discovered incidentally. Patients with incidentally discovered were identified and retrospectively reviewed. “Incidental” was defined as a finding on imaging that was obtained for a reason not attributable to the glioma, such as trauma, headache or neurological symptoms not attributable to the lesion. 327 patients between the ages of 19-83 underwent surgery for DIGs. Eleven (4m, 7f) patients (11/367 3.00%) were identified that harboured incidental gliomas. The most common reasons for head imaging were imaging performed to evaluate symptoms attributable to a different problem (4, 36.4%), imaging screening for other diagnoses (4, 36.4%) and trauma (3, 25%). Pathology of these lesions (based on WHO 2007 criteria) included Astrocytoma GrII (n=3), Oligodendroglioma GrII (n=4), Oligoastrocytoma GrII (n=2), and Glioblastoma (n=2). Eight tumors (8/11, 72.7%) harboured IDH1 (R132H) mutations. Two patients underwent biopsies only due to large size and extensive infiltration of the tumors, the 9 remaining patients underwent surgical resection of their tumors. No patients in this small cohort experienced post-operative complications. This retrospective cohort of incidentally discovered surgically managed DIGs demonstrates that the majority of lesions are IDH mutated and low grade. Incidentally discovered lesions are usually of small size and can be managed with low surgical risk. Ongoing follow-up will be required in order to determine progression-free and overall survival in this group of patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".