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Record W4399785672 · doi:10.1093/neuonc/noae064.405

LGG-12. INTEGRATED CLINICAL AND MOLECULAR CHARACTERIZATION OF 200 DISSEMINATED PEDIATRIC LOW-GRADE GLIOMAS

2024· article· en· W4399785672 on OpenAlexaff
Adrian Levine, Julie Bennett, Cyril Li, Mansuba Rana, Richard Yuditskiy, I-Chen Ho, Joseline Haizel‐Cobbina, Jordan R. Hansford, Amanda Luck, Louise Ludlow, David D. Eisenstat, Helen Toledano, Roaya M Masoud, Kohei Fukuoka, Kai Yamasaki, Yoshiko Nakano, Naureen Mushtaq, Khurram Minhas, Syed Ibrahim Bukhari, Ana Guerreiro Stuecklin, Annette Weiser, Filip Jadrijevic-Cvilje, Adam Fleming, Shawde Campbell, Chantel Cacciotti, Craig Erker, Frank K.H. van Landeghem, Bev Wilson, Karina Black, Mary MacNeil, Sylvia Cheng, Christopher Dunham, Valérie Larouche, Panagiota Giannakouros, Adriana Fonseca, Lane Williamson, Ashley Plant, Jean M. Mulcahy Levy, Samantha J DeMarsh, J. Vega, Prabhumallikarjun Patil, MacLean P. Nasrallah, Mariarita Santi-Vicini, Ernest Nelson, Richard Graham, Scott Raskin, Igor Fernandes, Michael C. Dewan, Uri Tabori, Cynthia Hawkins

Bibliographic record

VenueNeuro-Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversité LavalQueen Elizabeth II Health Sciences CentreUniversity of AlbertaIzaak Walton Killam Health CentreWestern UniversityMcMaster Children's HospitalBC Children's HospitalDalhousie UniversityHamilton Health SciencesStollery Children's HospitalHospital for Sick Children
Fundersnot available
KeywordsCharacterization (materials science)MedicineMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Abstract Pediatric low-grade gliomas (PLGG) have excellent outcomes overall but are a major clinical challenge when disseminated. Diffuse leptomeningeal glioneuronal tumor (DLGNT) is a recognised entity both clinically and pathologically, however many disseminated LGG (DLGG) fall outside this diagnosis. To better understand the clinical and molecular features of DLGG as well as risk factors for dissemination, we assembled an international consortium of 30 sites, contributing over 200 patients with clinical annotation, along with genomic and methylation profiling. DLGG have worse progression-free (PFS) and overall survival (OS) than PLGG overall (p<0.0001). Seventy (35%) presented with a localized mass and secondary dissemination, including some with initial gross-total resection. The most common growth pattern observed (n=77, 40%) is a dominant suprasellar/optic pathway tumor with leptomeningeal drop-metastases involving the brainstem and spinal cord. Only 27 (14%) patients had diffuse tumors (without an identifiable dominant mass), and these had significantly worse OS (p=0.03). The most common pathologic diagnosis was pilocytic/pilomyxoid astrocytoma (n=92; 53%). DLGNT comprised a minority of cases (n=23, 14%), with a trend (p=0.056) towards worse survival compared to other diagnoses. The most frequent molecular alteration was BRAF fusion (78/151 with molecular testing; 52%), followed by FGFR mutations or fusions (18/151; 12%). BRAF V600E was underrepresented (14/151; 9%). 1p deletion was rare (17 patients) but highly associated with DLGNT. Methylation classification (n=70) was highly concordant with histologic diagnoses rather than clinical behavior. Importantly, patients who received upfront targeted therapies (TT; BRAF and/or MEK inhibition, n=9) had better PFS compared to those receiving chemotherapy (n=146, p=0.05). Furthermore, patients who were treated sequentially with chemo then TT had longer PFS on TT (p=0.011). This study presents the largest cohort of DLGG to date, expanding our understanding of the clinical, pathologic, and molecular features of this disease and supports the use of upfront targeted therapy.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.330
Teacher spread0.311 · 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
Published2024
Admission routes1
Has abstractyes

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