Evidence map›Paper›PMID 31590408›Full record

ArticleInternational journal of molecular sciences2019

An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy.

Shin-Wha Lee, Ha-Young Lee, Hyo Joo Bang, Hye-Jeong Song, Sek Won Kong, Yong-Man Kim

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 15% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
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  3. Review
  4. Article
  5. Article
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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

6 authors at 4 institutions in 2 countries.

Shin-Wha LeeDepartment of Obstetrics & Gynecology, University of Ulsan, ASAN Medical Center, Seoul 05505, Korea. swhlee@amc.seoul.kr.ORCID 0000-0002-5088-1905
Ha-Young LeeASAN Institute for Life Science, ASAN Medical Center, Seoul 05505, Korea. leehayoung@gmail.com.
Hyo Joo BangAhngook Pharmaceutical Co., Ltd., Seoul 07445, Korea. jwnme@ahn-gook.com.
Hye-Jeong SongBio-IT Research Center, Hallym University, Chuncheon, Gangwon-do 24252, Korea. hjsong@hallym.ac.kr.
Sek Won KongComputational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA. sekwon.kong@childrens.harvard.edu.
Yong-Man KimDepartment of Obstetrics & Gynecology, University of Ulsan, ASAN Medical Center, Seoul 05505, Korea. ymkim@amc.seoul.kr.
Asan Medical Center · KRBoston Children's Hospital · USDaewoong Pharmaceutical (South Korea) · KRHallym University · KR

Funding

Ministry for Health, Welfare and Family affairs, Republic of Korea 0920010the Korea Health Industry Development Institute (KHIDI) HI06C0868the Ministry of Knowledge Economy, Science and Technology N0000425
6 · The paper itself

Abstract

This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples withOC(mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772-0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714-0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool.

Indexed as

AdultAgedBiomarkers, TumorFemaleHumansMiddle AgedModels, StatisticalOvarian NeoplasmsPrealbuminVascular Cell Adhesion Molecule-1WAP Four-Disulfide Core Domain Protein 2Biomarkers, TumorPrealbuminTTR protein, humanVascular Cell Adhesion Molecule-1WAP Four-Disulfide Core Domain Protein 2WFDC2 protein, humanovarian cancerprediction modelurinary biomarker

Identifiers

PMID31590408
PMCPMC6801627
OpenAlexW2978696849

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.