Evidence map›Paper›PMID 32676167›Full record

ArticleOncotarget2020

Correction of the NSE concentration in hemolyzed serum samples improves its diagnostic accuracy in small-cell lung cancer.

Sylvia A A M Genet, Esther Visser, Ben E E M van den Borne, Maggy Youssef-El Soud, Huub N A Belderbos, Gerben Stege, Marleen E A de Saegher, Federica Eduati, Maarten A C Broeren, Joost van Dongen and 3 more

Registry-linked trialOpen access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06105177 (Implementation of Up-front ctDNA Analysis Into Lung Cancer Care and Development of Liquid Biopsy-based Decision Support Models - the Lungmarker² Study), which is not on this map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.6field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06105177 recruitingnot on this mapstarted 2023, after this paper: background citation

Implementation of Up-front ctDNA Analysis Into Lung Cancer Care and Development of Liquid Biopsy-based Decision Support Models - the Lungmarker² Study

Typeobservational_patient_registrySponsorCatharina Ziekenhuis EindhovenRan2023 to 2026Enrolled800ConditionsLung Cancer
3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 7 institutions in 1 country.

Sylvia A A M GenetLaboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.
Esther VisserLaboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.
Ben E E M van den BorneCatharina Hospital Eindhoven, Eindhoven, The Netherlands.
Maggy Youssef-El SoudMáxima Medical Center, Eindhoven/Veldhoven, The Netherlands.
Huub N A BelderbosAmphia Hospital, Breda, The Netherlands.
Gerben StegeSint Anna Hospital, Geldrop, The Netherlands.
Marleen E A de SaegherSint Jans Gasthuis, Weert, The Netherlands.
Federica EduatiLaboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.
Maarten A C BroerenMáxima Medical Center, Eindhoven/Veldhoven, The Netherlands.
Joost van DongenLaboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.
Luc BrunsveldLaboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.
Daan van de KerkhofCatharina Hospital Eindhoven, Eindhoven, The Netherlands.
Volkher ScharnhorstLaboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands.
Eindhoven Cancer Registry · NLEindhoven University of Technology · NLCatharina Ziekenhuis · NLAmphia Ziekenhuis · NLMáxima Medisch Centrum · NLSt. Anna Ziekenhuis · NLSt.Jans Gasthuis Weert · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuron-specific enolase (NSE) is a well-known biomarker for the diagnosis, prognosis and treatment monitoring of small-cell lung cancer (SCLC). Nevertheless, its clinical applicability is limited since serum NSE levels are influenced by hemolysis, leading to falsely elevated results. Therefore, this study aimed to develop a hemolysis correction equation and evaluate its role in SCLC diagnostics. Two serum pools were spiked with increasing amounts of hemolysate obtained from multiple individuals. A hemolysis correction equation was obtained by analyzing the relationship between the measured NSE concentration and the degree of hemolysis. The equation was validated using intentionally hemolyzed serum samples, which showed that the correction was accurate for samples with an H-index up to 30 μmol/L. Correction of the measured NSE concentration in patients suspected of lung cancer caused an increase in AUC and a significantly lower cut-off value for SCLC detection when compared to uncorrected results. Therefore, a hemolysis correction equation should be used to correct falsely elevated NSE concentrations. Results of samples with an H-index above 30 μmol/L should not be reported to clinicians. Application of the equation illustrates the importance of hemolysis correction in SCLC diagnostics and questions the correctness of the currently used diagnostic cut-off value.

Indexed as

hemolysis correction equationneuron-specific enolaseprotein tumor markerssmall-cell lung cancer

Identifiers

PMID32676167
PMCPMC7343637
OpenAlexW3041843693

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.