Evidence map›Paper›PMID 38628667›Full record

ArticleFrontiers in oncology2024

Uniportal video-assisted thoracoscopic segmentectomy for fetal adenocarcinoma lung cancer with severe pulmonary emphysema: a case report.

Yun-Sheng Wu, Yu-Ting Chen, Jen-Hao Chuang, Hsien-Chi Liao

Open access · goldAbstract readCase Reports
In one paragraph

Article in Frontiers in oncology, 2024. 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, top 92% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

4 authors at 2 institutions in 1 country.

Yun-Sheng WuDepartment of Medical Education, National Taiwan University Hospital, Taipei, Taiwan.
Yu-Ting ChenDepartment of Pathology, National Taiwan University Cancer Center, Taipei, Taiwan.
Jen-Hao ChuangDivision of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan.
Hsien-Chi LiaoDepartment of Traumatology, National Taiwan University Hospital, Taipei, Taiwan.
National Taiwan University · TWNational Taiwan University Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Fetal adenocarcinoma is a very rare subtype of lung adenocarcinoma. Its incidence ranges from 0.1 to 0.87% among all primary lung neoplasms. Low-grade types tend to appear in the younger generation, and the age ranges from 20 to 50 years with a mean age of around 35 years. Surgical resection is currently the best way to treat fetal adenocarcinoma lung cancer without distant metastasis. Case report: This is a 56-year-old female who underwent low-dose computer tomography (LDCT) screening during the health examination. She used to be a heavy smoker for more than 30 years, and the CT images revealed severe bronchiectasis and emphysema. There is a solitary nodule with a diameter of 18.9 x 17.8mm in the central area of the left upper lobe. We decided to conduct left upper lobe S1~S3 segmentectomy under uniportal VATS. The surgery was successful, and the patient was discharged within one week and recovered well. The final diagnosis was fetal adenocarcinoma, low-grade (pT1cN0Mx, stage IA3). Conclusion: The first case reported as fetal adenocarcinoma lung cancer who underwent uniportal video-assisted thoracoscopic segmentectomy. We believe it is a safe and feasible procedure for low-grade types fetal adenocarcinoma patient with poor pulmonary function.

Indexed as

case reportfetal adenocarcinomalung adenocarcinomapulmonary emphysemauniportal video-assisted thoracoscopic

Identifiers

PMID38628667
PMCPMC11018926
OpenAlexW4393443063

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.