Evidence map›Paper›PMID 35912193›Full record

ArticleFrontiers in oncology2022

Case Report: An Unusual Case of Pulmonary Metastatic Adenocarcinoma From Low-Grade Appendiceal Mucinous Neoplasms.

Xin-Yu Zhao, Chun-Qiang Li, Shi-Yao Zhang, Gang Liu

Open access · goldAbstract readCase Reports
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

7 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Contract to kill: GNAS mutation.Molecular cancer · 2025
    Review
  5. Article
  6. Article
  7. Article
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 1 institution in 1 country.

Xin-Yu ZhaoDepartment of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Chun-Qiang LiDepartment of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Shi-Yao ZhangDepartment of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Gang LiuDepartment of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Tianjin Medical University General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Low-grade appendiceal mucinous neoplasms (LAMNs) are indolent tumors with low-grade cytology. Although peritoneal dissemination is common due to tumor rupture and mucinous deposits on the visceral peritoneal surface, distant involvement, such as lung, is rarely seen due to lack of invasiveness. Case Presentation: A 70-year-old woman presented to the hospital due to continuously elevated carcinoembryonic antigen (CEA) levels for 10 months without any symptoms. PET/CT revealed two lesions located in the left lung and appendix. The postoperative pathology results revealed pulmonary mucinous adenocarcinoma and LAMN. Then we performed next-generation sequencing (NGS) to clarify the relationship between the two tumors. The sequencing result showed that both tumors harbored the common tumor mutations, KRAS (p.G12D), GNAS (p.R201H), and BRAF (p.R735Q), which indicated that the pulmonary tumor was a metastasis of LAMN. Conclusion: This case is unusual in that the primary LAMN and the pulmonary metastasis are present at the time of diagnosis. This study reported the first pulmonary metastasis from LAMN verified by NGS.

Indexed as

appendiceal tumorcase reportlow-grade appendiceal mucinous neoplasmnext-generation sequencingpulmonary metastases

Identifiers

PMID35912193
PMCPMC9327614
OpenAlexW4285083274

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.