Evidence map›Paper›PMID 36212411›Full record

ArticleFrontiers in oncology2022

Combining prostate-specific antigen density with prostate imaging reporting and data system score version 2.1 to improve detection of clinically significant prostate cancer: A retrospective study.

Yin Lei, Tian Jie Li, Peng Gu, Yu Kun Yang, Lei Zhao, Chao Gao, Juan Hu, Xiao Dong Liu

Abstract read
In one paragraph

Article in Frontiers in oncology, 2022. 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
–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

1 citing paper in PubMed.

  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

8 authors.

Yin LeiDepartment of Urology, The First People's Hospital of Shuangliu District, Chengdu, China.
Tian Jie LiSchool of Clinical Medicine, Tsinghua University, Beijing, China.
Peng GuDepartment of Urology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Yu Kun YangMedical school, University of Electronic Science and Technology of China, Chengdu, China.
Lei ZhaoMedical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Chao GaoMedical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Juan HuMedical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Xiao Dong LiuDepartment of Urology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Globally, Prostate cancer (PCa) is the second most common cancer in the male population worldwide, but clinically significant prostate cancer (CSPCa) is more aggressive and causes to more deaths. The authors aimed to construct the risk category based on Prostate Imaging Reporting and Data System score version 2.1 (PI-RADS v2.1) in combination with Prostate-Specific Antigen Density (PSAD) to improve CSPCa detection and avoid unnecessary biopsy. Univariate and multivariate logistic regression and receiver-operating characteristic (ROC) curves were performed to compare the efficacy of the different predictors. The results revealed that PI-RADS v2.1 score and PSAD were independent predictors for CSPCa. Moreover, the combined factor shows a significantly higher predictive value than each single variable for the diagnosis of CSPCa. According to the risk stratification model constructed based on PI-RADS v2.1 score and PSAD, patients with PI-RADS v2.1 score of ≤2, or PI-RADS V2.1 score of 3 and PSA density of <0.15 ng/mL

Indexed as

clinically significant prostate cancer (csPCa)prostate biopsyprostate cancerprostate imaging reporting and data system scoreprostate-specific antigen density (PSAD)

Identifiers

PMID36212411
PMCPMC9539128

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Registered trials

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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.