Evidence map›Paper›PMID 32547202›Full record

ReviewCancer management and research2020

The Diagnosis, Treatment, Prognosis and Molecular Pathology of Borderline Ovarian Tumors: Current Status and Perspectives.

Yu Sun, Juan Xu, Xuemei Jia

Open access · goldAbstract readReview
In one paragraph

Review in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
7.2field-weighted citation impact, top 3% 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Yu SunDepartment of Gynecology, Women's Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing 210004, People's Republic of China.
Juan XuDepartment of Gynecology, Women's Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing 210004, People's Republic of China.ORCID 0000-0002-1516-5022
Xuemei JiaDepartment of Gynecology, Women's Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing 210004, People's Republic of China.ORCID 0000-0002-9323-4425
Nanjing Maternity and Child Health Care Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Borderline ovarian tumors (BOTs) are a type of low malignant potential tumor that is typically associated with better outcomes than ovarian cancer. Indeed, its 10-year survival rate is as high as 95%. However, there is a small subset of patients who experience relapse and eventually die. It has been shown that the prognosis of BOTs was based on pathological diagnosis, the age at diagnosis, pre-operative carbohydrate antigen 125 level, invasive implants, and micropapillary patterns. Now the molecular-targeted therapy and molecular-genetic diagnosis have developed into a form of precision medicine. Recent studies on extensive molecular characterizations and molecular pathological mechanisms of BOTs have helped us understand the genomic landscapes of BOTs, and therefore BOTs could be reclassified into biologically and clinically more accurate and effective subtypes. The purpose of this review is to summarize current status for the diagnosis and treatment of BOTs and to describe the research progress on molecular pathologies, with a goal of providing a theoretical perspective for the diagnosis and treatment of BOTs.

Indexed as

borderline ovarian tumordiagnosismolecular characterizationprognosis

Identifiers

PMID32547202
PMCPMC7246309
OpenAlexW3025505222

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.