Evidence map›Paper›PMID 34222428›Full record

ArticleWorld journal of clinical cases2021

Histological transformation of non-small cell lung cancer: Clinical analysis of nine cases.

Cai-Bao Jin, Ling Yang

Open access · diamondAbstract read
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Article in World journal of clinical cases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
1.6field-weighted citation impact, top 16% 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

11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 17 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

2 authors at 1 institution in 1 country.

Cai-Bao JinDepartment of Thoracic Oncology, Hubei Cancer Hospital, Wuhan 430000, Hubei Province, China.
Ling YangDepartment of Thoracic Oncology, Hubei Cancer Hospital, Wuhan 430000, Hubei Province, China. 348711624@qq.com.
Hubei Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHistological transformation is one of the numerous mechanisms of acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Given its rarity, the underlying transformational mechanisms, clinical features, and therapeutic prognoses are only studied through limited case reports.

aimTo analyze the clinical characteristics and underlying mechanisms in non-small cell lung cancer (SCLC) patients with histological transformation after treatment with EGFR-TKIs.

methodsWe retrospectively investigated nine patients diagnosed with non-SCLC transforming to SCLC, large-cell neuroendocrine carcinoma (LCNEC), or squamous cell carcinoma on re-biopsy after first- or third-generation EGFR-TKIs.

resultsThe median age of nine patients was 60 years. Among them, six patients had the EGFR 19del mutation, one had the L858R mutation, and one had wild-type EGFR. The level of plasma NSE was measured in six patients with SCLC or LCNEC transformation when transformation occurred, and five patients had elevated plasma NSE levels. All patients received standard chemotherapy after transformation with the exception of one patient who received chemotherapy and anlotinib.

conclusionTumor re-biopsy should be performed routinely when EGFR-TKI therapy fails in lung cancer patients to avoid ignoring histological transformation and to select a subsequent therapeutic strategy. The transformed tumor retained the original EGFR mutation, indicating that histological transformation represents an evolution from the initial tumor.

Indexed as

Epidermal growth factor receptor mutationEpidermal growth factor receptor tyrosine kinase inhibitorsHistological transformationNon-small cell lung cancerTumor re-biopsy

Identifiers

PMID34222428
PMCPMC8223818
OpenAlexW3169610796

What OpenQuestion holds

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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.