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Objective <p>To 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.</p> Methods <p>This 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&#xa0;ng/mL and 10–20&#xa0;ng/mL.</p> Results <p>Of 192 patients, 73 were classified as csPCa. Multivariable analysis confirmed PSAD (OR = 1.80, <i>P</i> = 0.008) and PI-RADS (OR = 4.50, <i>p</i> &lt; 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&#xa0;ng/mL (AUC = 0.805) and 10–20&#xa0;ng/mL (AUC = 0.855) PSA ranges.</p> Conclusion <p>PSAD 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&#xa0;ng/mL.</p>

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

  • 期刊论文

  • 发表日期：

    2025年12月18日

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

摘要

Objective

To 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.

Methods

This 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.

Results

Of 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.

Conclusion

PSAD 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.