Evidence map›Paper›PMID 21886665›Full record

ReviewDeutsches Arzteblatt international2011

The morphological and molecular diagnosis of lung cancer.

Iver Petersen

Abstract readReview
In one paragraph

Review in Deutsches Arzteblatt international, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

31 citing papers in PubMed, 63 citations in OpenAlex.

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  18. Determination of the target population in early benefit assessments in Germany: challenges for non-small-cell lung cancer.The European journal of health economics : HEPAC : health economics in prevention and care · 2020
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Iver PetersenInstitut für Pathologie, Universitätsklinikum Jena, Ziegelmühlenweg 1, 07743 Jena, Germany.
Jena University Hospital · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn Germany, lung cancer causes more deaths than any other malignant disease. Its main etiology is smoking, but other risk factors need to be considered as well. The morphological, molecular and biological phenotype is complex and should no longer be just categorized as either small-cell or non-small cell lung cancer.

methodsThis review article is based on the authors' longstanding involvement in the scientific investigation and diagnostic evaluation of lung cancer, including contributions to the current WHO classification and collaboration in the new interdisciplinary classification of adenocarcinoma. The relevant literature was selectively reviewed.

resultsLung cancer is morphologically classified into four main subtypes-small-cell carcinoma, squamous-cell carcinoma, adenocarcinoma, and large-cell carcinoma. Genetic and molecular analyses have revealed distinct differences within subtypes; in particular, adenocarcinomas can be further subdivided. Complex techniques of genomic analysis are now available, but clinicopathological data are still the most important determinants of prognosis and are clearly better for this purpose than molecular classification alone. Nonetheless, the assessment of specific molecular markers is becoming increasingly important.

conclusionThe morphological and molecular classification of lung cancer is undergoing a re-evaluation which will lead to more accurate assessment of individual prognoses and to improved prediction of the response to specific treatment regimens.

Indexed as

Molecular Diagnostic TechniquesAdenocarcinomaCarcinoma, Small CellCarcinoma, Squamous CellChromosome AberrationsGenetic MarkersGenotypeHumansLungLung NeoplasmsNeoplasm StagingPloidiesPrognosisGenetic Markers

Identifiers

PMID21886665
PMCPMC3163783
OpenAlexW2099596714

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.