Evidence map›Paper›PMID 39026982›Full record

ArticleFrontiers in oncology2024

Integration of PSAd and multiparametric MRI to forecast biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml.

Lei Ren, Yanling Chen, Zixiong Liu, Guankai Huang, Weifeng Wang, Xu Yang, Baohua Bai, Yan Guo, Jian Ling, Xiaopeng Mao

Abstract read
In one paragraph

Article in Frontiers in oncology, 2024. 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. Overdiagnosis and Overtreatment in Prostate Cancer.Diseases (Basel, Switzerland) · 2025
    Review
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

10 authors.

Lei Ren *Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China.
Yanling Chen *Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China.
Zixiong Liu *Department of Urology, The Seventh Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, China.
Guankai Huang *Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China.
Weifeng WangDepartment of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China.
Xu YangDepartment of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China.
Baohua BaiDepartment of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China.
Yan GuoDepartment of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China.
Jian LingDepartment of Radiology, The Eastern Hospital of the First Affiliated Hospital of Sun Yat-sen University, Sun Yat-Sen University, Guangzhou, China.
Xiaopeng MaoDepartment of Urology, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aims to investigate whether the transrectal ultrasound-guided combined biopsy (CB) improves the detection rates of prostate cancer (PCa) and clinically significant PCa (csPCa) in biopsy-naïve patients. We also aimed to compare the Prostate Imaging Reporting and Data System (PI-RADS v2.1) score, ADC values, and PSA density (PSAd) in predicting csPCa by the combined prostate biopsy. Methods: This retrospective and single-center study included 389 biopsy-naïve patients with PSA level 4~20 ng/ml, of whom 197 underwent prebiopsy mpMRI of the prostate. The mpMRI-based scores (PI-RADS v2.1 scores and ADC values) and clinical parameters were collected and evaluated by logistic regression analyses. Multivariable models based on the mpMRI-based scores and clinical parameters were developed by the logistic regression analyses to forecast biopsy outcomes of CB in biopsy-naïve patients. The ROC curves measured by the AUC values, calibration plots, and DCA were performed to assess multivariable models. Results: The CB can detect more csPCa compared with TRUSB (32.0% vs. 53%). The Spearman correlation revealed that Gleason scores of the prostate biopsy significantly correlated with PI-RADS scores and ADC values. The multivariate logistic regression confirmed that PI-RADS scores 4, 5, and prostate volume were important predictors of csPCa. The PI-RADS+ADC+PSAd (PAP) model had the highest AUCs of 0.913 for predicting csPCa in biopsy-naïve patients with PSA level 4~20 ng/ml. When the biopsy risk threshold of the PAP model was greater than or equal to 0.10, 51% of patients could avoid an unnecessary biopsy, and only 5% of patients with csPCa were missed. Conclusion: The prebiopsy mpMRI and the combined prostate biopsy have a high CDR of csPCa in biopsy-naïve patients. A multivariable model based on the mpMRI-based scores and PSAd could provide a reference for clinicians in forecasting biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml and make a more comprehensive assessment during the decision-making of the prostate biopsy.

Indexed as

mpMRIPI-RADSprostate biopsyprostate cancerprostate-specific antigen density

Identifiers

PMID39026982
PMCPMC11254766

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