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Objectives <p>The 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.</p> Materials and methods <p>The 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.</p> Results <p>51 patients (8.4%) were diagnosed with csPCa. TZ-PSAD demonstrated significant superior discriminative ability (AUC = 0.718) compared to PSAD (AUC = 0.686; <i>p</i> = 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.</p> Conclusions <p>TZ-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.</p> Critical relevance statement <p>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.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Our AI system enables accurate and reproducible segmentation and measurement of prostate zones.</p> </ItemContent> <ItemContent> <p>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.</p> </ItemContent> <ItemContent> <p>TZ-PSAD represents an effective triage metric to reduce unwarranted systematic biopsies in MRI-negative patients.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Jiaheng Shang,
  • Jingyun Wu,
  • Ruiyi Deng,
  • Meixia Shang,
  • Pengsheng Wu,
  • Jianhui Qiu,
  • Jingcheng Zhou,
  • Lin Cai,
  • Xiaoying Wang,
  • Kan Gong,
  • Yi Liu

摘要

Objectives

The 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 methods

The 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.

Results

51 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.

Conclusions

TZ-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.

Graphical Abstract