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Record W4403513063 · doi:10.1093/neuonc/noae144.005

PL02.3.A MOLECULAR CLASSIFICATION TO REFINE SURGICAL AND RADIOTHERAPEUTIC DECISION MAKING IN MENINGIOMA

2024· article· en· W4403513063 on OpenAlexaff
James Z. Wang, Vikas Patil, A P Landry, Chloe Gui, Andrew Ajisebutu, Jin Liu, Olli Saarela, Stephanie L. Pugh, Minhee Won, Zeel Patel, Rebeca Yakubov, Ramneet Kaloti, Christopher M. Wilson, Aaron Cohen‐Gadol, Mohamed A. Zaazoue, Ghazaleh Tabatabai, Marcos Tatagiba, Felix Behling, D A Almiron, Eric C. Holland, Tim J. Kruser, Jill S. Barnholtz‐Sloan, Andrew E. Sloan, Craig Horbinski, S Chotai, Lola B. Chambless, Andrew Gao, Alexander D. Rebchuk, Serge Makarenko, Stephen Yip, Felix Sahm, Sybren L. N. Maas, Derek S. Tsang, T, Leland Rogers, Kenneth Aldape, Farshad Nassiri, Gelareh Zadeh

Bibliographic record

VenueNeuro-Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsUniversity of British ColumbiaUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMeningiomaMedicineMedical physicsComputer scienceRadiology

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Treatment of the tumor and dural margin, most commonly with surgery and sometimes radiation, are cornerstones of therapy for meningioma. Molecular classifications have provided insights into the biology of disease. However, response to treatment remains heterogeneous. We compiled and generated clinical data on 2824 total meningiomas with molecular data on 1686 retrospective tumors and 100 prospective tumors from the RTOG-0539 phase-II clinical trial (NCT00895622) to define biomarkers of treatment response across molecular classifications. MATERIAL AND METHODS Data were collected from 10 different institutions. Meningiomas were re-graded in accordance with 2021 WHO classification based on the presence of CDKN2A/B deletion and TERT promoter mutation and stratified to Molecular Groups based on previously published methodologies utilizing DNA methylation alone or combined with RNA sequencing. Only molecularly-defined meningiomas based on the DKFZ central nervous system classifier were included in molecular classification and analysis. Propensity score matching (PSM) was used to mimic a randomized control trial for comparison of treatment arms (GTR vs STR, Simpson Grade 1/2 vs 3, Simpson Grade 1 vs 2, RT vs Observation). RESULTS Gross tumor resection (GTR) was associated with significantly longer progression-free-survival (PFS) across all Molecular Groups, and longer overall survival (OS) in Proliferative meningiomas (STR vs GTR HR 1.90, 95%CI 1.28-2.82, p=0.00155). Following PSM of key covariates including WHO grade, tumor location, and Molecular Group, the addition of dural margin treatment (Simpson grade 1 or 2) prolonged PFS compared to GTR alone (Simpson grade 3; HR 1.64, 95%CI 1.03-2.62, p=0.038). These results were reproduced when other molecular classifications and prognostic systems were utilized in PSM as well. When considering the effect of adjuvant radiotherapy (RT) following PSM, Immunogenic, NF2-wt, and Hypermetabolic meningiomas obtained a PFS benefit from the addition of RT but Proliferative meningiomas were RT-resistant. These findings were validated in meningiomas from the RTOG-0539 trial. To obtain an individualized prediction of RT-responsiveness, we leveraged RT-treated meningiomas from the RTOG-0539 trial (intermediate- and high-risk treatment arms in the trial) to build a DNA methylation and gene-expression model that was able to predict response to RT better than standard of care clinical covariates including WHO grade (AUC 0.81, 95%CI 0.72-0.90 vs 0.67, 95%CI 0.57-0.77) in an independent validation cohort of RT-treated meningiomas. CONCLUSION This study highlights the potential for molecular profiling and classification to meaningfully refine surgical and RT decision-making and supports the design of future molecularly-informed clinical trials.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.365
Teacher spread0.329 · 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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