MétaCan
Menu
Back to cohort
Record W4388588586 · doi:10.1093/neuonc/noad179.0682

PATH-52. MENINGIOMA MOLECULAR GROUPS PREDICT RESPONSE TO SURGERY AND RADIOTHERAPY

2023· article· en· W4388588586 on OpenAlexaff
Justin Z. Wang, Vikas Patil, Alexander Landry, Andrew Ajisebutu, Jeff Liu, Rebeca Yakubov, Andrew Gao, Aaron Cohen‐Gadol, Christopher M. Wilson, Silky Chotai, Eric C. Holland, Jill S. Barnholtz‐Sloan, Ghazaleh Tabatabai, Serge Makarenko, Stephen Yip, Felix Sahm, Sheila Mansouri, Qingxia Wei, Kenneth Aldape, Farshad Nassiri, Gelareh Zadeh

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsUniversity of British ColumbiaToronto Western HospitalPrincess Margaret Cancer CentreMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsAdjuvantMeningiomaMedicineMethylationOncologyProportional hazards modelInternal medicineAdjuvant therapyRadiation therapySurgeryCancerBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION Aside from surgery, radiotherapy (RT) remains the only accepted treatment for meningiomas. However, patient selection for adjuvant RT remains controversial. We aimed to evaluate whether novel molecular and methylation groups could more accurately predict outcomes following surgery and adjuvant RT compared to WHO grade using a large clinical-molecular dataset. Method: We utilized publicly available, clinically annotated molecular datasets of meningiomas and generated de novo DNA-methylation and RNA-sequencing data. Meningiomas were classified into different molecular and methylation groups based on the methodologies of their original publications (Nassiri et al. [2021], Bayley et al.[2022], Choudhury et al.[2022], Sahm et al.[2017]) and progression-free survival (PFS) following surgery with and without adjuvant RT were analyzed. RESULTS A total of 2029 meningiomas from 11 different institutions with adjuvant RT data were included. Adjuvant RT was associated with improved PFS in WHO grade 1 meningiomas following subtotal resection(STR) and WHO grade 2 meningiomas following gross total resection(GTR) or STR. Multivariable analysis using Cox proportional hazards regression models showed that having a meningioma belonging to a hypermetabolic (MG3)(HR 3.63, 95%CI 1.47-8.99), or proliferative (MG4)(HR 7.24, 95%CI 2.84-18.49) molecular group were associated with worse PFS following surgery and adjuvant RT when controlling for age, gender, WHO grade, and extent of resection. Meningiomas belonging to DKFZ MC-intermediate, MC-malignant and UCSF hypermitotic methylation groups were also associated with worse outcomes. These relationships held true even in subgroup analysis with only WHO grade 2 meningiomas and following propensity score matching for the same clinical covariates above. Nearly all molecular and methylation classifications predicted 5-year PFS including following surgery and adjuvant RT (area under the curve [AUC] 0.61-0.72) better than WHO grade (AUC 0.51). CONCLUSIONS Molecular classification improves clinical outcome prediction for meningiomas following surgery and adjuvant radiotherapy, supporting the rationale for molecularly informed treatment decisions and potential clinical trial stratification.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.298
Teacher spread0.273 · 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

Explore more

Same venueNeuro-OncologySame topicMeningioma and schwannoma managementFrench-language works237,207