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Record W2164503858 · doi:10.1056/nejmoa1115050

Fibulin-3 as a Blood and Effusion Biomarker for Pleural Mesothelioma

2012· article· en· W2164503858 on OpenAlexafffund
Harvey I. Pass, Stephen M. Levin, Michael Harbut, Jonathan Melamed, Luis Chiriboga, Jessica Donington, Margaret E. Huflejt, Michele Carbone, David Chia, Lee Goodglick, Gary E. Goodman, Mark Thornquist, Geoffrey Liu, Marc de Perrot, Ming‐Sound Tsao, Chandra Goparaju

Bibliographic record

VenueNew England Journal of Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersNational Institutes of HealthOntario Ministry of Health and Long-Term CareYork UniversityNational Cancer InstitutePrincess Margaret Hospital Foundation
KeywordsMedicineBiomarkerMesotheliomaFibulinPleural effusionOncologyInternal medicineStage (stratigraphy)PathologyRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: New biomarkers are needed to detect pleural mesothelioma at an earlier stage and to individualize treatment strategies. We investigated whether fibulin-3 in plasma and pleural effusions could meet sensitivity and specificity criteria for a robust biomarker. METHODS: We measured fibulin-3 levels in plasma (from 92 patients with mesothelioma, 136 asbestos-exposed persons without cancer, 93 patients with effusions not due to mesothelioma, and 43 healthy controls), effusions (from 74 patients with mesothelioma, 39 with benign effusions, and 54 with malignant effusions not due to mesothelioma), or both. A blinded validation was subsequently performed. Tumor tissue was examined for fibulin-3 by immunohistochemical analysis, and levels of fibulin-3 in plasma and effusions were measured with an enzyme-linked immunosorbent assay. RESULTS: Plasma fibulin-3 levels did not vary according to age, sex, duration of asbestos exposure, or degree of radiographic changes and were significantly higher in patients with pleural mesothelioma (105±7 ng per milliliter in the Detroit cohort and 113±8 ng per milliliter in the New York cohort) than in asbestos-exposed persons without mesothelioma (14±1 ng per milliliter and 24±1 ng per milliliter, respectively; P<0.001). Effusion fibulin-3 levels were significantly higher in patients with pleural mesothelioma (694±37 ng per milliliter in the Detroit cohort and 636±92 ng per milliliter in the New York cohort) than in patients with effusions not due to mesothelioma (212±25 and 151±23 ng per milliliter, respectively; P<0.001). Fibulin-3 preferentially stained tumor cells in 26 of 26 samples. In an overall comparison of patients with and those without mesothelioma, the receiver-operating-characteristic curve for plasma fibulin-3 levels had a sensitivity of 96.7% and a specificity of 95.5% at a cutoff value of 52.8 ng of fibulin-3 per milliliter. In a comparison of patients with early-stage mesothelioma with asbestos-exposed persons, the sensitivity was 100% and the specificity was 94.1% at a cutoff value of 46.0 ng of fibulin-3 per milliliter. Blinded validation revealed an area under the curve of 0.87 for plasma specimens from 96 asbestos-exposed persons as compared with 48 patients with mesothelioma. CONCLUSIONS: Plasma fibulin-3 levels can distinguish healthy persons with exposure to asbestos from patients with mesothelioma. In conjunction with effusion fibulin-3 levels, plasma fibulin-3 levels can further differentiate mesothelioma effusions from other malignant and benign effusions. (Funded by the Early Detection Research Network, National Institutes of Health, and others.).

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)0.002
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.302
Teacher spread0.280 · 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

Citations281
Published2012
Admission routes2
Has abstractyes

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