Evidence map›Paper›PMID 31080486›Full record

ArticleCytoJournal2019

Subtyping of non-small cell lung cancer by cytology specimens: A proposal for resource-poor hospitals.

Betul Celik, Tangul Bulut, Andras Khoor

Open access · diamondAbstract read
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Article in CytoJournal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.7field-weighted citation impact, top 15% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 19 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

3 authors at 3 institutions in 2 countries.

Betul CelikAddress: Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, Florida, 32224, USA.
Tangul BulutDepartment of Pathology, Antalya Hospital, Health Science University, Turkey.
Andras KhoorAddress: Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, Florida, 32224, USA.
Antalya Bilim University · TRMayo Clinic in Florida · USSağlık Bilimleri Üniversitesi · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimCancer diagnosis and treatment depend on pathology reports but naming a cancer is sometimes impossible without specialized techniques. We aimed to evaluate the sensitivity of cytological sub-classification of non-small cell lung carcinoma, not otherwise specified group (NSCLC-NOS) into Adenocarcinoma (AC) and Squamous cell carcinoma (SqCC) without using immunohistochemistry.

methodsEndobronchial ultrasound guided fine-needle aspiration biopsies and cytology slides diagnosed as NSCLC-NOS between 2004- 2008 were reviewed retrospectively. The final diagnosis was reached by immunohistochemistry (TTF-1, p63) when necessary.

resultsOne hundred-twenty nine cases were retrieved. The final diagnoses were as follows: SqCC: 30.3%; AC: 65.7%; combined tumor (3 adenosquamous and 1 small cell + SqCC): 4%. Cytological diagnoses rendered were as follow: Definitely SqCC: 10.1%; favor SqCC: 14.1%; definitely AC: 38.4%; favor AC: 35.4%; NSCLC-NOS: 2%. The sensitivity and specificity of cytology were 86.3 and 87.5% for AC diagnosis respectively.

conclusionPositive and negative predictive value of cytology was 95.3% and it was even 100% for well to moderately differentiated tumors. There was a tendency to sub-classify poorly differentiated SqCC as AC. Papanicolaou stain increased the diagnostic accuracy of SqCC. The combined tumor rate was 4% and after recognizing a tumor component, the second component was missed if the slide examination was terminated prematurely.

Indexed as

Cytologylung carcinomanon-small cell lung carcinomapoor-resource yesresource-poor is bettersub-classification

Identifiers

PMID31080486
PMCPMC6501614
OpenAlexW2940981851

What OpenQuestion holds

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LicenceCC BY-NC-SA
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Registered trials

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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.