Evidence map›Paper›PMID 29531580›Full record

ArticleOpen access Macedonian journal of medical sciences2018

Histopathological Pattern and Age Distribution, of Malignant Ovarian Tumor among Sudanese Ladies.

Sumeya A Kheiri, Abdelillah Kunna, Ali Yousif Babiker, Sultan A Alsuhaibani, Rami Yousif Ahmed, Mohamed Alkhatim Alsammani

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Article in Open access Macedonian journal of medical sciences, 2018. 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
0.7field-weighted citation impact, top 24% 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Sumeya A KheiriDepartment of Obstetrics and Gynecology, College of Medicine, University of Bahri, Khartoum, Sudan.
Abdelillah KunnaDepartment of Obstetrics and Gynecology, College of Medicine, University of Bahri, Khartoum, Sudan.
Ali Yousif BabikerDepartment of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraidah, Saudi Arabia.
Sultan A AlsuhaibaniDepartment of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraidah, Saudi Arabia.
Rami Yousif AhmedDepartment of Obstetrics and Gynecology, College of Medicine, University of Bahri, Khartoum, Sudan.
Mohamed Alkhatim AlsammaniDepartment of Obstetrics and Gynecology, College of Medicine, University of Bahri, Khartoum, Sudan.
Qassim University · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionOvarian cancer is the cause of a high case-fatality ratio, and most of the cases are diagnosed in late stages.

objectivesTo determine the histopathological types, age distribution, and ovarian tumour stages among diagnosed with ovarian cancer at Al - Amal Tower a multi-referral polyclinic of Radiology & Isotope Center Khartoum (RICK), Sudan.

methodsAll histopathology reports patients' case from January to June 2015 were reviewed. The cancers classified according to federation international of Obstetrics and Gynecology (FIGO).

resultsThere were 127 cases of ovarian cancers. Surface epithelial cancers were the most common 77.7% (n = 98), followed by sex cord-stromal cancers 11.23% (n = 14), Germ cell tumor 1.6% (n = 2). Metastatic cancers were seen from colon and breast in 6.3% and 3.9 % of cases respectively. Few cases (14%) of ovarian cancers were reported before 40 years of age, after the age of 50 is a sharp increase in the incidence of a tumour. The mean age at presentation was 52.36 ± 14.210 years, there is mean age of menarche 13.59 ± 2.706 years. Very few patients used HRT (1.6%) or had been on ovulation induction treatment (8.7%). Most of patients 39 (30.7%) presented in stage IIIC, and stage 1V 32 (25.2%) indicating a poor prognosis.

conclusionThe incidence of different types of ovarian cancers in the present study is similar to worldwide incidence. The surface epithelial tumour is the commonest ovarian cancer, of which serous adenocarcinoma is the commonest and most of our patients present in late stages.

Indexed as

CancerIncidenceNeoplasmOvarianStagingWomen age

Identifiers

PMID29531580
PMCPMC5839424
OpenAlexW2794229981

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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.