Evidence map›Paper›PMID 35664560›Full record

ArticleJournal of oncology2022

Clinical Analysis of 137 Cases of Ovarian Tumors in Pregnancy.

Qi Yin, Min Zhong, Zhihui Wang, XiuJie Sheng

Abstract read
In one paragraph

Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Developing and validating ultrasound-based machine-learning models incorporating radiomics features to predict malignancy in adnexal masses.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026
    Observational
  3. 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

4 authors.

Qi YinDepartment of Obstetrics and Gynecology, Center for Reproductive Medicine/Department of Fetal Medicine and Prenatal Diagnosis, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150 Guangdong, China.
Min ZhongDepartment of Obstetrics and Gynecology, Center for Reproductive Medicine/Department of Fetal Medicine and Prenatal Diagnosis, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150 Guangdong, China.
Zhihui WangDepartment of Obstetrics and Gynecology, Center for Reproductive Medicine/Department of Fetal Medicine and Prenatal Diagnosis, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150 Guangdong, China.
XiuJie ShengDepartment of Obstetrics and Gynecology, Center for Reproductive Medicine/Department of Fetal Medicine and Prenatal Diagnosis, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150 Guangdong, China.ORCID https://orcid.org/0000-0003-3850-0812

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian tumors do not really typically occur in association with pregnant; however, once they do, the treatment is critical. It is important to note that around 6% of ovarian tumors in pregnancies are cancerous. The problems induced by ovarian tumors in pregnancy particularly necessitate rapid medical intervention and are much more frequent than cancer. Medication choices and survival of ovary tumor patients could be influenced by varied diagnoses of ovarian masses. So, we present an upgraded logistic regression (ULR) approach in this paper. Initially, the collection of 137 patient datasets was employed in screening test to identify the ovarian tumor as benign-tumor and malignant-tumor by using contrast-enhanced ultrasonography (CEU) method. Then, the screening test images are preprocessed using wavelet transform (WT) approach. The preprocessed data are extracted by using local binary pattern (LBP) and laws' texture energy (LTE) techniques. Finally, the clinical analysis of the ovarian tumor can be obtained by the proposed ULR approach. The performances were examined and compared with existing approaches to achieve the proposed approach with greatest correctness. The findings are depicted by utilizing the MATLAB tool.

Identifiers

PMID35664560
PMCPMC9159870

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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.