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Purpose <p>To develop and validate a scoring system that combines clinical and radiological variables to predict the likelihood of clinically significant prostate cancer (csPCa, which is defined as Gleason Grade group ≥ 2) before biopsy and stratify patients by predicted risk.</p> Methods <p>This retrospective study enrolled 788 patients. Data were stratified into a derivation cohort and two validation cohorts by institutions. Imaging evaluation included: Prostate Imaging Reporting and Data System v2.1 (PI-RADS v2.1), Simplified PI-RADS [S-PI-RADS, incorporating qualitative and quantitative assessment of diffusion restriction degree and lesion volume (LV) on DWI/ADC sequences], prostate volume (PV), LV, and the longest to shortest diameter ratio (LD/SD). %fPSA was the ratio of free prostate-specific antigen (PSA) to total PSA (tPSA). The adjusted PSA (aPSA) was derived as: aPSA = tPSA×(LV/PV). Independent csPCa predictors were determined through multivariate regression analysis and transformed into the scoring system. Diagnostic performance of PI-RADS, S-PI-RADS, and the scoring system were compared using ROC analysis. The scoring system was stratified into four risk categories based on total scores.</p> Results <p>The scoring system—integrating %fPSA, S-PI-RADS, aPSA, and LD/SD—demonstrated robust predictive performance for csPCa across all derivation and validation cohorts (AUC: 0.891, 0.875, and 0.897, respectively), comparable to PI-RADS (AUC: 0.861, 0.880, and 0.865; all <i>P</i> &gt; 0.05). It achieved the highest PPV among three systems in all cohorts (0.780, 0.832, and 0.722). Median predicted probabilities for the low (0–2 points), intermediate-low (3–7), intermediate-high (8–12), and high-risk groups (13–17) were 3.8%, 14.4%, 67.8%, and 90.3%, respectively, aligning with observed risks. While inter-reader agreement for PI-RADS was suboptimal between trained and untrained radiologists (κ = 0.643), the scoring system showed stronger consensus (κ = 0.808).</p> Conclusion <p>This scoring system demonstrating comparable diagnostic performance to PI-RADS and improving PPV, highlighting its potential clinical practicability.</p>

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A comprehensive scoring system integrating clinical and radiological variables for the detection of clinically significant prostate cancer on bi-parameter MRI: multi-center comparison with multi-parametric MRI

  • 期刊论文

  • 发表日期：

    2025年6月19日

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  • Liqin Yang,
  • Zhenwei Ding,
  • Xu Wang,
  • Zhiping Li,
  • Fawei Huang,
  • Yitao Liu,
  • Chenyang Xu,
  • Pengfei Jin

摘要

Purpose

To develop and validate a scoring system that combines clinical and radiological variables to predict the likelihood of clinically significant prostate cancer (csPCa, which is defined as Gleason Grade group ≥ 2) before biopsy and stratify patients by predicted risk.

Methods

This retrospective study enrolled 788 patients. Data were stratified into a derivation cohort and two validation cohorts by institutions. Imaging evaluation included: Prostate Imaging Reporting and Data System v2.1 (PI-RADS v2.1), Simplified PI-RADS [S-PI-RADS, incorporating qualitative and quantitative assessment of diffusion restriction degree and lesion volume (LV) on DWI/ADC sequences], prostate volume (PV), LV, and the longest to shortest diameter ratio (LD/SD). %fPSA was the ratio of free prostate-specific antigen (PSA) to total PSA (tPSA). The adjusted PSA (aPSA) was derived as: aPSA = tPSA×(LV/PV). Independent csPCa predictors were determined through multivariate regression analysis and transformed into the scoring system. Diagnostic performance of PI-RADS, S-PI-RADS, and the scoring system were compared using ROC analysis. The scoring system was stratified into four risk categories based on total scores.

Results

The scoring system—integrating %fPSA, S-PI-RADS, aPSA, and LD/SD—demonstrated robust predictive performance for csPCa across all derivation and validation cohorts (AUC: 0.891, 0.875, and 0.897, respectively), comparable to PI-RADS (AUC: 0.861, 0.880, and 0.865; all P > 0.05). It achieved the highest PPV among three systems in all cohorts (0.780, 0.832, and 0.722). Median predicted probabilities for the low (0–2 points), intermediate-low (3–7), intermediate-high (8–12), and high-risk groups (13–17) were 3.8%, 14.4%, 67.8%, and 90.3%, respectively, aligning with observed risks. While inter-reader agreement for PI-RADS was suboptimal between trained and untrained radiologists (κ = 0.643), the scoring system showed stronger consensus (κ = 0.808).

Conclusion

This scoring system demonstrating comparable diagnostic performance to PI-RADS and improving PPV, highlighting its potential clinical practicability.