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Record W4399787348 · doi:10.1093/neuonc/noae064.297

HGG-13. INFANT-TYPE HEMISPHERIC GLIOMA (IHG): AN INDIVIDUAL PATIENT DATA META-ANALYSIS

2024· article· en· W4399787348 on OpenAlexaff
Lara Chavaz, Aditi Bagchi, Fabienne Toutain, Stefan M. Pfister, Dominik Sturm, Torsten Pietsch, Gerrit H. Gielen, Andreas Waha, Matthew Clarke, Michael Karremann, Martin Benesch, Thomas Perwein, Gunther Nussbaumer, Christof M. Kramm, Maura Massimino, Veronica Biassoni, Maria Vinci, Angela Mastronuzzi, Dannis G. van Vuurden, Sophie E. M. Veldhuijzen van Zanten, Alan Mackay, Chris Jones, David Jones, Ana Guerreiro Stücklin, Uri Tabori, Cynthia Hawkins, Scott Ryall, Andrés Morales La Madrid, Álvaro Lassaletta, Simon Bailey, Darren Hargrave, Jason Chiang, Moatasem El‐Ayadi, Bruna Minniti Mançano, Rui Manuel Reis, Christian Hagel, Giles Robinson, Amar Gajjar, Hamza Gorsi, Nicolas Silvestrini, André O. von Bueren

Bibliographic record

VenueNeuro-Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicineOncologyInternal medicineMultivariate analysisGliomaCohortRadiation therapyFusion geneChemotherapyGeneCancer researchBiology

Abstract

fetched live from OpenAlex

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 RESULTS Hundred-fifty-five previously reported and one unpublished IHG were identified: 131 (84%) had fusions in RTK genes, of which ALK was most prevalent (62/131), followed by NTRK1/2/3 (30/131), ROS1 (30/131), and MET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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.034
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.115
GPT teacher head0.366
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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