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Objectives <p>To develop and validate a logistic regression model and point-based scoring system for predicting ≥ Grade Group 3 clinically significant prostate cancer using a combination of multiparametric MRI findings and patient risk factors.</p> Methods <p>This Institutional Review Board-approved, retrospective cohort study was conducted1/1/2022-12/31/2022 with data analysis 7/1/2023-6/30/2025. Males undergoing prostate multiparametric MRI during the study period at a multi-institutional health system with prostate-specific antigen ≥ 4ng/mL and prostate biopsy and/or radical prostatectomy within 6 months post-multiparametric MRI were included in the study. A separate derivation cohort included 960 men who underwent multiparametric MRI from 2015 to 2019. A logistic regression and point-based scoring system for predicting high-grade clinically significant prostate cancer (≥ Grade Group 3) was developed using predictors including prostate-specific antigen density (PSAD), highest PI-RADS score from multiparametric MRI, extraprostatic extension, and age groups (e.g., 65 ≤ 70). Discrimination was assessed using area under the curve.</p> Results <p>1245 patients met inclusion criteria; 83% were White; 86% were ≥ 60 years of age. 83% had a focal lesion with PI-RADS score of ≥ 3. Based on the new point-based scoring system for predicting high-grade (≥ Grade Group 3) prostate cancer, ~ 28% of patients had a cumulative score of 0–7, with an estimated clinically significant prostate cancer risk of 9%. The area under the curve was 0.77 for both the logistic regression model and the point-based system.</p> Conclusions <p>In a multi-institutional health system, age, as well as prostate-specific antigen density, highest PI-RADS score from multiparametric MRI, and extraprostatic extension significantly predicted high-grade clinically significant prostate cancer in a logistic regression and point-based scoring system.</p>

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Predicting high-grade clinically significant prostate cancer

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Ronilda Lacson,
  • Elias Kikano,
  • Arya Haj-Mirzaian,
  • Hanjoo Lee,
  • Kristine Burk,
  • Sonia Gaur,
  • Adam Kibel,
  • Ramin Khorasani

摘要

Objectives

To develop and validate a logistic regression model and point-based scoring system for predicting ≥ Grade Group 3 clinically significant prostate cancer using a combination of multiparametric MRI findings and patient risk factors.

Methods

This Institutional Review Board-approved, retrospective cohort study was conducted1/1/2022-12/31/2022 with data analysis 7/1/2023-6/30/2025. Males undergoing prostate multiparametric MRI during the study period at a multi-institutional health system with prostate-specific antigen ≥ 4ng/mL and prostate biopsy and/or radical prostatectomy within 6 months post-multiparametric MRI were included in the study. A separate derivation cohort included 960 men who underwent multiparametric MRI from 2015 to 2019. A logistic regression and point-based scoring system for predicting high-grade clinically significant prostate cancer (≥ Grade Group 3) was developed using predictors including prostate-specific antigen density (PSAD), highest PI-RADS score from multiparametric MRI, extraprostatic extension, and age groups (e.g., 65 ≤ 70). Discrimination was assessed using area under the curve.

Results

1245 patients met inclusion criteria; 83% were White; 86% were ≥ 60 years of age. 83% had a focal lesion with PI-RADS score of ≥ 3. Based on the new point-based scoring system for predicting high-grade (≥ Grade Group 3) prostate cancer, ~ 28% of patients had a cumulative score of 0–7, with an estimated clinically significant prostate cancer risk of 9%. The area under the curve was 0.77 for both the logistic regression model and the point-based system.

Conclusions

In a multi-institutional health system, age, as well as prostate-specific antigen density, highest PI-RADS score from multiparametric MRI, and extraprostatic extension significantly predicted high-grade clinically significant prostate cancer in a logistic regression and point-based scoring system.