Evidence map›Paper›PMID 41665841›Full record

ArticleInsights into imaging2026

Artificial intelligence-derived transition zone PSA density as a triage tool to reduce unnecessary prostate systematic biopsies in MRI-negative men.

Jiaheng Shang, Jingyun Wu, Ruiyi Deng, Meixia Shang, Pengsheng Wu, Jianhui Qiu, Jingcheng Zhou, Lin Cai, Xiaoying Wang, Kan Gong and 1 more

Abstract read
In one paragraph

Article in Insights into imaging, 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. 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

11 authors.

Jiaheng Shang *Department of Urology, Peking University First Hospital, Beijing, China.
Jingyun Wu *Department of Radiology, Peking University First Hospital, Beijing, China.
Ruiyi DengDepartment of Urology, Peking University First Hospital, Beijing, China.
Meixia ShangDepartment of Biostatistics, Peking University First Hospital, Beijing, China.
Pengsheng WuBeijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
Jianhui QiuDepartment of Urology, Peking University First Hospital, Beijing, China.
Jingcheng ZhouDepartment of Urology, Peking University First Hospital, Beijing, China.
Lin CaiDepartment of Urology, Peking University First Hospital, Beijing, China.
Xiaoying WangDepartment of Radiology, Peking University First Hospital, Beijing, China.
Kan GongDepartment of Urology, Peking University First Hospital, Beijing, China. kan.gong@bjmu.edu.cn.ORCID http://orcid.org/0000-0001-7195-677X
Yi LiuDepartment of Urology, Peking University First Hospital, Beijing, China. liuyipkuhsc@163.com.ORCID http://orcid.org/0000-0002-6711-6601

Funding

Beijing Research Ward Excellence Program BRWEP2024W054070105National High Level Hospital Clinical Research Funding (Interdepartmental Research Project of Peking University First Hospital) 2023IR27
6 · The paper itself

Abstract

objectivesThe study aimed to assess the predictive performance of transition zone PSA density (TZ-PSAD) compared to conventional PSA density (PSAD) in detecting clinically significant prostate cancer (csPCa) among patients with negative pre-biopsy MRI findings. MATERIALS AND

methodsThe study included 606 patients with negative MRI findings who subsequently underwent transrectal ultrasound-guided systematic biopsy. AI software automatically measured prostate and zonal volumes, from which PSAD and TZ-PSAD (total PSA/transition zone volume) were calculated. Diagnostic performances were evaluated using ROC curve analysis, risk stratification was applied to select patients needing biopsy, and independent predictors of imaging-invisible csPCa were determined through univariate and multivariate analyses.

results51 patients (8.4%) were diagnosed with csPCa. TZ-PSAD demonstrated significant superior discriminative ability (AUC = 0.718) compared to PSAD (AUC = 0.686; p = 0.019). Patients with TZ-PSAD ≥ 0.35 ng/mL/cc had a csPCa detection rate of 20.1%, while those below this threshold had a rate of 4.1%. The optimal TZ-PSAD threshold of 0.35 ng/mL/cc showed superior performance than the guideline-recommended PSAD threshold of 0.2 ng/mL/cc. Multivariate analysis identified TZ-PSAD as a strong independent predictor of imaging-invisible csPCa.

conclusionsTZ-PSAD outperforms conventional PSAD in predicting csPCa among men with negative MRI, offering a valuable tool for risk stratification. This facilitates individualized risk assessment, potentially reducing unnecessary biopsies and optimizing patient management. CRITICAL RELEVANCE STATEMENT: Our AI system delivers accurate and reproducible prostate zone segmentation, while TZ-PSAD derived from AI outperforms conventional PSAD in detecting csPCa in MRI-negative patients and serves as an effective triage tool to optimize biopsy decision-making and reduce unnecessary systematic biopsies. KEY POINTS: Our AI system enables accurate and reproducible segmentation and measurement of prostate zones. TZ-PSAD demonstrates significantly superior diagnostic performance over conventional PSAD for identifying men with a negative MRI who will have csPCa on a systematic biopsy. TZ-PSAD represents an effective triage metric to reduce unwarranted systematic biopsies in MRI-negative patients.

Indexed as

Artificial intelligenceClinically significant prostate cancerNegative MRIProstate-specific antigen densityTransition zone

Identifiers

PMID41665841
PMCPMC12891307

What OpenQuestion holds

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LicenceCC BY
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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.