Evidence map›Paper›PMID 41410894›Full record

ArticleInternational urology and nephrology2026

Construction of a nomogram model for clinically significant prostate cancer based on biparametric magnetic resonance imaging.

Peng Wang, Guanghai Ji, Qing Liu, Yuanshan Yang, Ya Wang, Giacomo Sica, Junjie Liu, Sheng Xiao, Fei Liu, Qing Zhang

Abstract read
PubMed Publisher
In one paragraph

Article in International urology and nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Peng Wang *Department of Radiology, The First Affiliated Hospital of Yangtze University, No. 55, Jianghan North Road, Shashi District, Jingzhou City, 434000, Hubei Province, China.
Guanghai Ji *Department of Radiology, The First Affiliated Hospital of Yangtze University, No. 55, Jianghan North Road, Shashi District, Jingzhou City, 434000, Hubei Province, China.
Qing Liu *Oncology Department, Jiangling People's Hospital, Jingzhou, 434100, China.
Yuanshan YangNuclear Medicine Department, The First Affiliated Hospital of Yangtze University, Jingzhou, 434000, China.
Ya WangDepartment of Scientific Research Office, The First Affiliated Hospital of Yangtze University, Jingzhou, 434000, China.
Giacomo SicaDepartment of Radiology, Azienda Ospedaliera dei Colli, Monaldi Hospital, 80131, Naples, Italy.
Junjie LiuDepartment of Radiology, The First Affiliated Hospital of Yangtze University, No. 55, Jianghan North Road, Shashi District, Jingzhou City, 434000, Hubei Province, China.
Sheng XiaoDepartment of Radiology, The First Affiliated Hospital of Yangtze University, No. 55, Jianghan North Road, Shashi District, Jingzhou City, 434000, Hubei Province, China. 18163138930@163.com.
Fei LiuDepartment of Radiology, The First Affiliated Hospital of Yangtze University, No. 55, Jianghan North Road, Shashi District, Jingzhou City, 434000, Hubei Province, China. Liuxueyingfei@163.com.
Qing ZhangDepartment of Radiology, The First Affiliated Hospital of Yangtze University, No. 55, Jianghan North Road, Shashi District, Jingzhou City, 434000, Hubei Province, China. zq672089068@126.com.

Funding

Jingzhou Science and Technology Plan Project 2025HD13
6 · The paper itself

Abstract

objectiveTo evaluate and compare the diagnostic performance of clinical features and PI-RADS v2.1 for identifying clinically significant prostate cancer (csPCa) and to assess whether a combined model improves detection.

methodsThis retrospective study analyzed patients who underwent biparametric MRI (bpMRI). csPCa was defined as ISUP grade ≥ 2 with pathological T-stage ≥ pT2a (prostatectomy) or PI-RADS score ≥ 4 (biopsy). PI-RADS v2.1 scores were assigned independently. PSAD was calculated as tPSA/prostate volume. Univariate and multivariable logistic regression identified predictors, with performance assessed by ROC analysis. Subgroup analyses were conducted for tPSA 4-10 ng/mL and 10-20 ng/mL.

resultsOf 192 patients, 73 were classified as csPCa. Multivariable analysis confirmed PSAD (OR = 1.80, P = 0.008) and PI-RADS (OR = 4.50, p < 0.001) as independent predictors. The combined PSAD + PI-RADS model achieved an AUC of 0.830 (95% CI 0.801-0.869), with 83.6% sensitivity and 78.2% specificity. Subgroup analyses showed consistent high performance in both the 4-10 ng/mL (AUC = 0.805) and 10-20 ng/mL (AUC = 0.855) PSA ranges.

conclusionPSAD and PI-RADS v2.1 are independent predictors of csPCa under a composite definition. Their combination provides superior diagnostic accuracy, offering an effective strategy for risk stratification and biopsy decision-making in patients with tPSA 4-20 ng/mL.

Indexed as

Magnetic Resonance ImagingMultiparametric Magnetic Resonance ImagingNomogramsProstatic NeoplasmsAgedHumansMaleMiddle AgedRetrospective StudiesBiparametric magnetic resonance imagingNomogram modelProstate cancer

Identifiers

What OpenQuestion holds

Textmetadata
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.