HGG-13. INFANT-TYPE HEMISPHERIC GLIOMA (IHG): AN INDIVIDUAL PATIENT DATA META-ANALYSIS
Bibliographic record
Abstract
Abstract BACKGROUND Infant-type hemispheric gliomas (IHG) are epigenetically distinct pediatric high-grade gliomas characterized by fusions in receptor tyrosine kinase (RTK) genes. METHODS We performed a methodical literature search, including 30 publications (22 case reports), to identify patients who met the diagnostic criteria of IHG based on the 2021 WHO Classification of CNS Tumors. Individual patient data were obtained from published literature and/or via the authors of the publications. Survival analysis was conducted using the Kaplan-Meier method, and multivariate analysis was performed to investigate the effect of clinical and molecular variables on outcomes. RESULTS Hundred-fifty-five previously reported and one unpublished IHG were identified: 131 (84%) had fusions in RTK genes, of which ALK was most prevalent (62/131), followed by NTRK1/2/3 (30/131), ROS1 (30/131), and MET (9/131). Twenty-five patients, with either no identified RTK fusion (6/156) or not assessable fusion (19/156), had methylation scores ≥ 0.9 for IHG (using the Molecular Neuropathology brain tumor classifier versions ≥ 11b4). Surgery followed by adjuvant chemotherapy in 69% (67/97) was the most common primary treatment used in our cohort. The 3-year EFS and OS were 55% (95%CI: 45-67) and 81 % (95%CI: 73-89). 41 patients with relapsed or progressive tumors received various second-line treatments including surgery, chemotherapy, radiotherapy, and targeted therapy. Based on multivariate analysis, complete resection resulted in better EFS (p = 0.05) and OS (p = 0.009), and the presence and type of RTK gene fusion were not associated with clinical outcomes. CONCLUSION Our results show that despite favorable OS, patients with IHG often show early progression, indicating that the primary optimal treatment for IHG is yet to be established. Our analysis further indicates that achieving a safe, complete resection may play an important role in treating these patients. A comprehensive analysis of the salvage regimen is required to understand their role in OS.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.034 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".