Evidence map›Paper›PMID 33847772›Full record

ReviewAbdominal radiology (New York)2021

Current update on the molecular genetics and management of hereditary ovarian cancers: a primer for radiologists.

Malak Itani, Neeraj Lalwani, Dhakshinamoorthy Ganeshan, Maria Zulfiqar, Cary Siegel

Abstract readReview
PubMed Publisher
In one paragraph

Review in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 43% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

5 authors at 3 institutions in 1 country.

Malak ItaniMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
Neeraj LalwaniVirginia Commonwealth University School of Medicine, Richmond, VA, 23298, USA. neeraj.lalwani@vcuhealth.org.ORCID http://orcid.org/0000-0001-5514-2805
Dhakshinamoorthy GaneshanDivision of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Maria ZulfiqarMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
Cary SiegelMallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
Washington University in St. Louis · USThe University of Texas MD Anderson Cancer Center · USVirginia Commonwealth University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

More than one-fifth of ovarian cancers are hereditary, with most of them caused by BRCA genes. Malignant ovarian neoplasms are primarily epithelial tumors, a heterogeneous group of tumors with variable genetic backgrounds that translate into different biologic behaviors and morphologic features. Radiologists play an increasingly important role in the diagnosis and management of oncology patients. Familiarity with hereditary ovarian cancers will have a positive impact on patient management and radiologists' involvement in the multidisciplinary approach needed for this delicate patient population. In this paper, we review the basic histologic and genetic backgrounds of ovarian tumors with an emphasis on hereditary ovarian cancers, as well as the effects that these factors have on patient workup, primarily with regard to imaging studies.

Indexed as

Genetic Predisposition to DiseaseOvarian NeoplasmsCarcinoma, Ovarian EpithelialFemaleHumansMolecular BiologyRadiologistsBRCA mutationGorlin syndromeHereditary ovarian cancerLynch syndromePeutz-Jeghers syndrome

Identifiers

PMID33847772
OpenAlexW3152993695

What OpenQuestion holds

Textmetadata
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.