Evidence map›Paper›PMID 37582697›Full record

ArticleBMC medical imaging2023

Development and validation of a nomogram based on biparametric MRI PI-RADS v2.1 and clinical parameters to avoid unnecessary prostate biopsies.

Yunhan Wang, Lei Wang, Xiaohua Tang, Yong Zhang, Na Zhang, Biao Zhi, Xiangke Niu

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Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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7citing papers in PubMed
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1 · What the graph read from it

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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

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yunhan WangDepartment of Urology, Affiliated Hospital of Chengdu University, Chengdu, 610081, Sichuan, China.
Lei WangDepartment of Radiology, Ninety-Three Hospital, Jiangyou City, 610000, Sichuan, China.
Xiaohua TangDepartment of Radiology, Ninety-Three Hospital, Jiangyou City, 610000, Sichuan, China.
Yong ZhangDepartment of Radiology, DeYang People's Hospital, Deyang City, 610000, Sichuan, China.
Na ZhangDepartment of General Practice Medicine, Affiliated Hospital of Chengdu University, Chengdu, 610081, Sichuan, China.
Biao ZhiDepartment of Interventional Radiology, Affiliated Hospital of Chengdu University, Chengdu, 610081, Sichuan, China.
Xiangke NiuDepartment of Interventional Radiology, Affiliated Hospital of Chengdu University, Chengdu, 610081, Sichuan, China. niu19850519@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBiparametric MRI (bpMRI) is a faster, contrast-free, and less expensive MRI protocol that facilitates the detection of prostate cancer. The aim of this study is to determine whether a biparametric MRI PI-RADS v2.1 score-based model could reduce unnecessary biopsies in patients with suspected prostate cancer (PCa).

methodsThe patients who underwent MRI-guided biopsies and systematic biopsies between January 2020 and January 2022 were retrospectively analyzed. The development cohort used to derive the prediction model consisted of 275 patients. Two validation cohorts included 201 patients and 181 patients from 2 independent institutions. Predictive models based on the bpMRI PI-RADS v2.1 score (bpMRI score) and clinical parameters were used to detect clinically significant prostate cancer (csPCa) and compared by analyzing the area under the curve (AUC) and decision curves. Spearman correlation analysis was utilized to determine the relationship between International Society of Urological Pathology (ISUP) grade and clinical parameters/bpMRI score.

resultsLogistic regression models were constructed using data from the development cohort to generate nomograms. By applying the models to the all cohorts, the AUC for csPCa was significantly higher for the bpMRI PI-RADS v2.1 score-based model than for the clinical model in both cohorts (p < 0.001). Considering the test trade-offs, urologists would agree to perform 10 fewer bpMRIs to avoid one unnecessary biopsy, with a risk threshold of 10-20% in practice. Correlation analysis showed a strong correlation between the bpMRI score and ISUP grade.

conclusionA predictive model based on the bpMRI score and clinical parameters significantly improved csPCa risk stratification, and the bpMRI score can be used to determine the aggressiveness of PCa prior to biopsy.

Indexed as

Magnetic Resonance ImagingProstatic NeoplasmsHumansImage-Guided BiopsyMaleNomogramsProstateProstate-Specific AntigenRetrospective StudiesProstate-Specific AntigenBiopsyMagnetic resonance imagingMultivariable risk stratificationProstate cancerProstate Imaging Reporting and Data System

Identifiers

PMID37582697
PMCPMC10426075

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