Evidence map›Paper›PMID 40900881›Full record

ArticleCancer diagnosis & prognosis

Treatment Response and Survival in Thyroid Transcription Factor-1 Negative Non-squamous Non-small Cell Lung Cancer.

Yutaka Takahara, Ryudai Abe, Sumito Nagae, Takuya Tanaka, Yoko Ishige, Ikuyo Shionoya, Kouichi Yamamura, Masafumi Nojiri, Masaharu Iguchi

Abstract read
In one paragraph

Article in Cancer diagnosis & prognosis. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Yutaka TakaharaDepartment of Respiratory Medicine, Kanazawa Medical University, Ishikawa, Japan.
Ryudai AbeDepartment of Respiratory Medicine, Kanazawa Medical University, Ishikawa, Japan.
Sumito NagaeDepartment of Respiratory Medicine, Kanazawa Medical University, Ishikawa, Japan.
Takuya TanakaDepartment of Respiratory Medicine, Kanazawa Medical University, Ishikawa, Japan.
Yoko IshigeDepartment of Respiratory Medicine, Kanazawa Medical University, Ishikawa, Japan.
Ikuyo ShionoyaDepartment of Respiratory Medicine, Kanazawa Medical University, Ishikawa, Japan.
Kouichi YamamuraDepartment of Respiratory Medicine, Kanazawa Medical University, Ishikawa, Japan.
Masafumi NojiriDepartment of Respiratory Medicine, Kanazawa Medical University, Ishikawa, Japan.
Masaharu IguchiDepartment of Respiratory Medicine, Kanazawa Medical University, Ishikawa, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/Aim: In patients with thyroid transcription factor-1 (TTF-1)-negative non-squamous non-small cell lung cancer (NS-NSCLC), the efficacy of pemetrexed and immunotherapy has been reported to be limited, and the optimal treatment strategy remains unclear. Recent studies have suggested that bevacizumab may improve outcomes; however, robust evidence is still lacking. This study aimed to clarify the clinical characteristics of responders to first-line treatment in patients with TTF-1-negative NS-NSCLC and identify predictive factors for treatment response, proposing an optimal treatment strategy. Patients and Methods: A retrospective analysis was conducted on patients with TTF-1-negative NS-NSCLC. Patients were classified into responder and non-responder groups based on first-line treatment efficacy, and predictive factors associated with treatment response were analyzed. Results: Among the 29 patients included, seven (24.1%) were classified as responders. Platinum-based combination therapy was significantly more common in the responder group ( Conclusion: In the treatment of TTF-1-negative NS-NSCLC, platinum-based combination therapy, particularly with the addition of bevacizumab, improved response rates and led to prolonged survival.

Indexed as

bevacizumabfirst-line therapypredictive factorsTTF-1TTF-1-negative NS-NSCLC

Identifiers

PMID40900881
PMCPMC12401038

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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.