Evidence map›Paper›PMID 26970967›Full record

ReviewCancer metastasis reviews2016

Personalized treatment of advanced non-small-cell lung cancer in routine clinical practice.

Robert Pirker, Martin Filipits

Open access · hybridAbstract readReview
In one paragraph

Review in Cancer metastasis reviews, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.8field-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

13 citing papers in PubMed, 27 citations in OpenAlex.

  1. Simultaneous Occurrence ofJTO clinical and research reports · 2024
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  5. The co-mutation ofJournal of thoracic disease · 2022
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  13. Tumor-node-metastasis staging and treatment patterns of 73,167 patients with lung cancer in Brazil.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia
    Article
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

2 authors at 1 institution in 1 country.

Robert PirkerDepartment of Medicine I, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria. robert.pirker@meduniwien.ac.at.
Martin FilipitsDepartment of Medicine I, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
Medical University of Vienna · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Personalized treatment of patients with advanced non-small-cell lung cancer based on clinical and molecular tumor features has entered clinical routine practice. The 2015 pathological classification of lung cancer mandates immunohistochemical and molecular analysis. Therapeutic strategies focused on inhibition of angiogenesis and growth factor receptor signaling. Inhibitors of angiogenesis and monoclonal antibodies directed against the epidermal growth factor receptor have shown efficacy in combination with chemotherapy. Mutations in the epidermal growth factor receptor and anaplastic lymphoma kinase have become clinically relevant therapeutic targets. Immune checkpoint inhibitors are also entering routine clinical practice. Identification of predictive biomarkers is essential and faces several challenges including tumor heterogeneity and dynamic changes of tumor features over time. Liquid biopsies may overcome some of these challenges in the future.

Indexed as

Precision MedicineAntibodies, MonoclonalCarcinoma, Non-Small-Cell LungErbB ReceptorsHumansMolecular Targeted TherapyNeovascularization, PathologicProtein Kinase InhibitorsAntibodies, MonoclonalEGFR protein, humanErbB ReceptorsProtein Kinase InhibitorsALKBiomarkerCetuximabEGFR mutationsLiquid biopsyNecitumumab

Identifiers

PMID26970967
PMCPMC4821865
OpenAlexW2293682794

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.