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Record W4327931594 · doi:10.1055/s-0043-1762046

Olfactory Neuroblastoma: A Multicenter Survival Analysis and Application of a Staging Modification Incorporating Hyam's Grade

2023· article· en· W4327931594 on OpenAlexaff
Garret Choby, Mathew Geltzeiler, João Paulo Almeida, Pierre‐Olivier Champagne, Justin S. Cetas, Erik Chan, Jeremy Ciporen, Mark B. Chaskes, Juan C. Fernandez‐Miranda, Paul A. Gardner, Fred Gentili, Peter H. Hwang, Keven Seung Yong Ji, Aristotelis Kalyvas, Keonho A. Kong, Ryan P. McMillan, Jamie O’Byrne, Chirag Patel, Zara M. Patel, Maria Peris‐Celda, Carlos Pinheiro‐Neto, Olabisi Sanusi, Carl H. Snyderman, Brian D. Thorp, Jamie J. Van Gompel, George Zenonos, Nate Zwaggerman, Eric W. Wang

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsUniversity of TorontoUniversité Laval
Fundersnot available
KeywordsEsthesioneuroblastomaMedicineStaging systemMalignancySurvival analysisNeuroblastomaOverall survivalOncologyRadiologySurgeryInternal medicineCancerRadiation therapy

Abstract

fetched live from OpenAlex

Background: Olfactory neuroblastoma (ONB) is a rare sinonasal malignancy with favorable survival and frequent delayed recurrence. Current staging systems including the Kadish, modified Kadish (mKadish), and Dulguerov systems poorly delineate locally advanced tumors and do not incorporate histologic grade. The two primary aims of this multi-institutional study were to 1) examine the clinical covariates associated with survival and recurrence of ONB in the modern era and 2) incorporate Hyam's tumor grade into existing staging systems and to assess its ability to predict survival and recurrence. Methods: Data from nine North American academic centers were retrospectively reviewed between 2005 and 2021. Patient demographics, tumor staging (original Kadish, modified Kadish [mKadish], Dulguerov, and AJCC staging systems), Hyam's grade, treatment factor, recurrence and survival were included. Univariate and multivariate analyses were completed to assess recurrence and survival. The ability of traditional staging systems and novel modifications of these were assessed for their capacity to predict recurrence and survival with concordance-statistics analysis. Results: A total of 256 ONB patients were included. 27 patients (10.9%) were Kadish A, 53 (21.4%) were Kadish B and 168 (67.7%) were Kadish C. 41 patients (16.3%) underwent surgery alone, 137 (54.4%) surgery + radiation therapy (RT), 51 (20.2%) surgery + chemotherapy + RT and 17 other (6.6%). The 5-year and 10-year overall survival (OS) were 83.5% and 66.7%, respectively. The 5-year and 10-year progression-free survival (PFS) were 70.8% and 53.1%, respectively. On univariate analysis, age, mKadish, Dulguerov stage, nodal status, positive margins, and surgery were associated with survival. On univariate analysis, T-stage, M-stage, AJCC stage, Kadish stage, Dulguerov stage, orbital involvement, skull base bone involvement, Hyam's grade and positive margins were associated with recurrence. On multivariable analysis, age, AJCC stage, involvement of bilateral maxillary sinuses and positive margins were associated with mortality, while only AJCC staging was associated with recurrence. When assessing the ability of staging systems to predict mortality, the original Kadish staging system had the worst predictive value (c-statistic = 0.5676), while a novel modification of the Dulguerov system incorporating Hyam's grade had the highest predictive value (c-statistic = 0.659), When assessing the ability to predict recurrence, the original mKadish had the worst ability to predict recurrence (c-statistic = 0.5513), while a novel modification of the AJCC staging incorporating Hyam's grade had the highest predictive value for recurrence (c-statistic = 0.702). Conclusions: Traditional ONB staging systems poorly predict survival and recurrence. Incorporation of Hyam's grade into traditional ONB staging systems improves the ability to predict mortality and disease recurrence. Publication History Article published online: 01 February 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

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)0.003
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 score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.072
GPT teacher head0.313
Teacher spread0.242 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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