Evidence map›Paper›PMID 41982681›Full record

ArticleTranslational lung cancer research2026

Retrospective study of alectinib treatment among variants of

Yuma Watanabe, Ayako Takigami, Shu Hisata, Masayuki Nakayama, Naoko Mato, Makoto Maemondo

Abstract read
In one paragraph

Article in Translational lung cancer research, 2026. 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
0cells of the map it votes in
0citing 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

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

6 authors.

Yuma WatanabeDepartment of Respiratory Medicine, Division of Internal Medicine, Jichi Medical University, Shimotsuke, Japan.ORCID https://orcid.org/0009-0008-3569-8687
Ayako TakigamiDepartment of Respiratory Medicine, Division of Internal Medicine, Jichi Medical University, Shimotsuke, Japan.ORCID https://orcid.org/0009-0002-7751-8601
Shu HisataDepartment of Respiratory Medicine, Division of Internal Medicine, Jichi Medical University, Shimotsuke, Japan.ORCID https://orcid.org/0000-0003-2830-755X
Masayuki NakayamaDepartment of Respiratory Medicine, Division of Internal Medicine, Jichi Medical University, Shimotsuke, Japan.
Naoko MatoDepartment of Respiratory Medicine, Division of Internal Medicine, Jichi Medical University, Shimotsuke, Japan.ORCID https://orcid.org/0000-0001-8435-2051
Makoto MaemondoDepartment of Respiratory Medicine, Division of Internal Medicine, Jichi Medical University, Shimotsuke, Japan.ORCID https://orcid.org/0009-0008-3569-8687

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Anaplastic lymphoma kinase ( Methods: We performed a retrospective analysis of patients treated with alectinib between January 2019 and December 2024. Results: A total of 11 patients (male: n=4, female: n=7) were enrolled (V1: n=4, V2: n=3, V3: n=4); their median age was 75 years. Clinical stages at diagnosis were: stage II (n=1), and stage IV (n=10). Intrapulmonary metastasis was common in patients with V3. The median maximum tumor reduction rates were 90.5% (range, 84.0-92.0%), 63.4% (range, 35.0-84.0%), and 98.0% (range, 67.8-100.0%) for patients with V1, V2, and V3, respectively. The best overall response was partial response (PR) in all patients with V1 and V2. Three patients with V3 had complete response (CR) and one had PR. The median PFS was 16.5 and 15.9 months for patients with V1 and V2, respectively, and not reached for those with V3. Conclusions: In this study, there was no significant difference in the therapeutic effect of alectinib by

Indexed as

alectinibanaplastic lymphoma kinase (ALK)Non-small cell lung cancer (NSCLC)

Identifiers

PMID41982681
PMCPMC13071716

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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.