Evidence map›Paper›PMID 35147976›Full record

ArticleTurk patoloji dergisi2022

Evidence for Diverse Prognosis in High-Grade Serous Ovarian Carcinoma: Solid, Pseudoendometrioid, and Transitional-Like; So-Called "SET Morphology" and Progesterone Receptor Status.

Halit Uner, Metin Demir, Dincer Goksuluk, Ayse Kars, Meral Uner, Alp Usubutun

Abstract read
In one paragraph

Article in Turk patoloji dergisi, 2022. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Overview of Tumor Heterogeneity in High-Grade Serous Ovarian Cancers.International journal of molecular sciences · 2023
    Review
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.

Halit UnerDepartment of Pathology, Hacettepe University School of Medicine, ANKARA, TURKEY.
Metin Demir
Dincer Goksuluk
Ayse Kars
Meral Uner
Alp Usubutun

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveHigh-grade serous ovarian carcinoma (HGSC) is one of the major tumors of the gynecological system with a poor survival rate and variable microscopic appearance. It was suggested that SET (solid, pseudo-endometrioid and transitional-like) morphology in ovarian HGSC is predictably associated with BRCA deficiencies. In this study, we investigated the microscopic patterns and some immunohistochemical markers predicting the prognosis of serous carcinoma. MATERIAL AND

methodWe re-evaluated 305 HGSC ovarian resections morphologically and calculated the SET morphology percentages for each case. Morphological and immunohistochemical data correlated with the survival and post-treatment disease progression data.

resultsThe median age at diagnosis was 57 years and the median follow-up period was 3.1 years. The median overall survival (OS) of ovarian carcinoma in SET-predominant tumors (n=60) was 81 months, while for tumors with SET non-dominant morphology (n=63) and non-SET morphology (n=182) it was 59.7 and 44.7 months, respectively.

conclusionPredominant (more than 50%) SET morphology was significantly associated with increased survival rates of HGSC. Immunohistochemically, p53, ERCC1, ER, and PR antibodies were applied and only PR antibody positivity was found to be associated with borderline statistical significance for increased survival rates. Our results suggest that SET morphology may be a potential predictive and prognostic marker in managing the treatment strategies of HGSC.

Indexed as

Cystadenocarcinoma, SerousOvarian NeoplasmsBiomarkers, TumorFemaleHumansNeoplasm GradingPrognosisReceptors, ProgesteroneBiomarkers, TumorReceptors, Progesterone

Identifiers

PMID35147976
PMCPMC10508410

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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.