Evidence map›Paper›PMID 30018881›Full record

ReviewFrontiers in oncology2018

Management of Brain Metastases in Epidermal Growth Factor Receptor Mutant Non-Small-Cell Lung Cancer.

William J Kelly, Neil J Shah, Deepa S Subramaniam

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 1 pooled it
7.7field-weighted citation impact, top 2% 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

56 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.

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  5. Molecular and clinical disparity ofTranslational lung cancer research · 2026
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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 1 institution in 1 country.

William J KellyDivision of Hematology-Oncology, Georgetown University, Washington, DC, United States.
Neil J ShahDivision of Hematology-Oncology, Georgetown University, Washington, DC, United States.
Deepa S SubramaniamDivision of Hematology-Oncology, Georgetown University, Washington, DC, United States.
Georgetown University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer remains a leading cause of mortality with 1.69 million deaths worldwide. Activating mutations in epidermal growth factor receptor (EGFR), predominantly exon 19 deletions and exon 21 L858R mutations, are known oncogenic drivers identified in 20-40% of non-small-cell lung cancers (NSCLC). 70% of EGFR-mutant NSCLC patients develop brain metastases (BM), compared to 38% in EGFR wild-type patients. First-generation tyrosine kinase inhibitors (TKIs), such as erlotinib and gefitinib have proven to be superior to chemotherapy in the front-line treatment of EGFR-mutant NSCLC, as has afatinib, a second-generation TKI. The most common acquired resistance mechanism is the development of a gatekeeper mutation in exon 20 T790M. Osimertinib has emerged as a third-generation EGFR TKI with proven activity in the front-line setting as well as in patients with a T790M acquired resistance mutation with remarkable CNS activity. As long-term survival outcomes in EGFR-mutant NSCLC continue to improve, the burden of BM becomes a greater challenge. Here, we review the literature related to the management of BM in EGFR-mutant NSCLC including the role of the three generations of EGFR TKIs, immunotherapy, and brain radiation.

Indexed as

brain metastasesepidermal growth factor receptornon-small-cell lung cancerosimertinibtargeted therapy

Identifiers

PMID30018881
PMCPMC6037690
OpenAlexW2807709930

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.