Evidence map›Paper›PMID 33804009›Full record

ArticleCancers2021

Novel Histologic Categorization Based on Lauren Histotypes Conveys Prognostic Information for Gastroesophageal Junction Cancers-Analysis from a Large Single Center Cohort in Germany.

Rebekka Schirren, Alexander Novotny, Julia Slotta-Huspenina, Helmut Friess, Daniel Reim

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.1field-weighted citation impact, top 52% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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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

5 authors.

Rebekka SchirrenDepartment of Surgery, TUM School of Medicine, Ismaninger Strasse 22, 81675 Munich, Germany.
Alexander NovotnyDepartment of Surgery, TUM School of Medicine, Ismaninger Strasse 22, 81675 Munich, Germany.ORCID 0000-0002-8029-5958
Julia Slotta-HuspeninaInstitute of Pathology, TUM School of Medicine, Ismaninger Strasse 22, 81675 Munich, Germany.
Helmut FriessDepartment of Surgery, TUM School of Medicine, Ismaninger Strasse 22, 81675 Munich, Germany.
Daniel ReimDepartment of Surgery, TUM School of Medicine, Ismaninger Strasse 22, 81675 Munich, Germany.ORCID 0000-0001-9736-3307

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adenocarcinoma of the gastroesophageal junction (AEG) ranks among the most common cancers in the Western world with increasing incidence. However, the prognostic influence and applicability of the Lauren classification was not examined in detail before. The purpose of this analysis was to analyze the oncologic outcomes of GE-junction cancer related to the Lauren histotype in a large single center cohort. Data from the prospectively documented database of the Klinikum Rechts der Isar (TUM School of Medicine) for patients undergoing curatively intended oncologic resection for GE-junction cancer between 1984 and 2018 were extracted. Univariate and multivariate regression analyses were performed to identify predictors for overall survival. Kaplan-Meier analyses were done to investigate the survival rates according to the Lauren histotype. After identification of two distinct histologic categories with prognostic implications, propensity score matching (PSM) was performed to balance for confounders and evaluate its oncologic outcomes retrospectively. In the time period indicated, 1710 patients were treated for GE-junction cancer. Exclusion criteria were: R2-resections (

Indexed as

gastric/gastroesophageal cancerLauren histotypeperioperative chemotherapy

Identifiers

PMID33804009
PMCPMC8002040
OpenAlexW3138434325

What OpenQuestion holds

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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.