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Record W4380368215 · doi:10.1093/neuonc/noad073.222

LGG-12. CLINICAL AND MOLECULAR FEATURES OF DISSEMINATED PEDIATRIC LOW-GRADE GLIOMA

2023· article· en· W4380368215 on OpenAlexaff
Adrian Levine, Cyril Li, Joseline Haizel‐Cobbina, Liana Nobre, Julie Bennett, Michael C. Dewan, Uri Tabori, Cynthia Hawkins

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsKRASMedicineGliomaPopulationOncologyMetastasisInternal medicineCancer researchCancerColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Although most pediatric LGG (PLGG) have excellent long-term survival, there is a subset of cases that disseminate throughout the neuraxis (DLGG) that have very poor outcomes. The reason for this aggressive behavior is unknown but we hypothesize that distinct and specific biological mechanisms underlie the metastatic ability. The methylation-based class of diffuse leptomeningeal glioneuronal tumor (DLGNT) is characterized by MAPK pathway activating fusions with chromosome 1p loss, 19q loss, and/or 1q gain. However, it is unknown what proportion of DLGG match the DLGNT group, and which other methylations classes are at risk of metastasis. To improve our understanding of this rare patient population, we created an international DLGG consortium. Data from the first 68 cases shows a broad age distribution and no sex predilection. As expected, DLGG has much worse prognosis than the overall PLGG population. Virtually all DLGG progress at 5 years, compared to a quarter of other PLGG, and DLGG are 5-times more likely to die at 10 years. We observe three patterns of dissemination – 35% present with a localized mass and have secondary dissemination, 50% with disseminated tumor and a clear dominant mass, and 15% with disseminated disease without a dominant mass. In 47 patients with molecular testing, BRAF fusions accounted for 64% of driver alterations. Additional alterations were identified less frequently, including BRAF V600E (9%), FGFR1 alterations (9%), and KRAS mutations (4%). In 25 patients with methylation profiling, 10 (40%) successfully classified with a calibrated score above 0.8, illustrating that many cases do not fit a defined methylation group. The most common methylation classification was pilocytic astrocytoma (5 patients), and surprisingly only two patients classified as DLGNT. In sum this study illustrates the variable clinical behaviour associated with metastasis in PLGG and expands the range of molecular driver alterations, including ones previously unreported in DLGNT.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.023
GPT teacher head0.351
Teacher spread0.328 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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