Evidence map›Paper›PMID 33489710›Full record

ArticleInternational cancer conference journal2021

Peritoneal dissemination of breast cancer diagnosed by laparoscopy.

Tsuyoshi Nakagawa, Goshi Oda, Akifumi Kikuchi, Toshifumi Saito, Tomoyuki Fujioka, Kazunori Kubota, Mio Mori, Iichiro Onishi, Hiroyuki Uetake

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International cancer conference journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 26% 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

3 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

9 authors at 1 institution in 1 country.

Tsuyoshi NakagawaDepartment of Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima Bunkyou-ku, Tokyo, 113-8519 Japan.ORCID 0000-0003-1967-0445
Goshi OdaDepartment of Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima Bunkyou-ku, Tokyo, 113-8519 Japan.
Akifumi KikuchiDepartment of Colorectal Surgery, Tokyo Medical and Dental University, Tokyo, Japan.
Toshifumi SaitoDepartment of Colorectal Surgery, Tokyo Medical and Dental University, Tokyo, Japan.
Tomoyuki FujiokaDepartment of Radiology, Tokyo Medical and Dental University, Tokyo, Japan.
Kazunori KubotaDepartment of Radiology, Tokyo Medical and Dental University, Tokyo, Japan.
Mio MoriDepartment of Radiology, Tokyo Medical and Dental University, Tokyo, Japan.
Iichiro OnishiDepartment of Pathology, Tokyo Medical and Dental University, Tokyo, Japan.
Hiroyuki UetakeDepartment of Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima Bunkyou-ku, Tokyo, 113-8519 Japan.
Tokyo Medical and Dental University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accuracy of modern imaging techniques for the diagnosis of peritoneal carcinomatosis is poor. A breast cancer patient with a high serum CA15-3 level did not receive a definitive diagnosis of peritoneal dissemination by imaging examination and then underwent laparoscopy. Pathological examination showed peritoneal dissemination of breast cancer, but the biological markers were different from the primary lesion: ER(-), PgR(-), and Her2:3 +. T-DM1 therapy was very effective, and her systemic symptoms disappeared. Since biomarkers of metastatic lesions may sometimes change, laparoscopic biopsy is very important and useful.

Indexed as

Breast cancerLaparoscopyPeritoneal dissemination

Identifiers

PMID33489710
PMCPMC7797395
OpenAlexW3103199316

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.