Evidence map›Paper›PMID 29910650›Full record

ReviewCurrent oncology (Toronto, Ont.)2018

Algorithm for the treatment of advanced or metastatic squamous non-small-cell lung cancer: an evidence-based overview.

N Daaboul, G Nicholas, S A Laurie

Abstract readReview
In one paragraph

Review in Current oncology (Toronto, Ont.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Review
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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.

N DaaboulDivision of Medical Oncology, The Ottawa Hospital Cancer Centre, University of Ottawa, Ottawa, ON.
G NicholasDivision of Medical Oncology, The Ottawa Hospital Cancer Centre, University of Ottawa, Ottawa, ON.
S A LaurieDivision of Medical Oncology, The Ottawa Hospital Cancer Centre, University of Ottawa, Ottawa, ON.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The treatment of squamous non-small-cell lung cancer (nsclc) is evolving. In the past, the backbone of treatment was chemotherapy, with very few other options available. Fortunately, that situation is changing, especially with a better understanding of tumour biology. Various strategies have been tried to improve patient outcomes. The most notable advance must be immunotherapy, which has revolutionized the treatment paradigm for lung cancer in patients without a driver mutation. Immunotherapy is now the treatment of choice in patients who have progressed after chemotherapy and is replacing chemotherapy as upfront therapy in a selected population. Other strategies have also been tried, such as the addition of targeted therapy to chemotherapy. Targeted agents include ramucirumab, an inhibitor of vascular endothelial growth factor receptor 2, and necitumumab, a monoclonal antibody against epithelial growth factor receptor. Recently, advances in molecular profiling have also been applied to tumours of squamous histology, in which multiple genetic alterations, including mutations and amplifications, have been described. Research is actively seeking targetable mutations and testing various therapies in the hopes of further improving prognosis for patients with squamous nsclc. Here, we review the various advances in the treatment of squamous nsclc and present a proposed treatment algorithm based on current evidence.

Indexed as

AlgorithmsEvidence-Based PracticeCarcinoma, Non-Small-Cell LungCarcinoma, Squamous CellDisease ProgressionHumansLung NeoplasmsMedical OncologyNeoplasm MetastasisPractice Patterns, Physicians'immunotherapyNon-small-cell carcinoma of the lungsquamous cell carcinomatargeted therapy

Identifiers

PMID29910650
PMCPMC6001772

What OpenQuestion holds

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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.