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Optimizing detection of clinically significant prostate cancer through nomograms incorporating mri, clinical features, and advanced serum biomarkers in biopsy naïve men

Prostate Cancer and Prostatic Diseases volume 26, pages 588–595 (2023)Cite this article

Subjects

Abstract

Purpose

To develop nomograms that predict the detection of clinically significant prostate cancer (csPCa, defined as ≥GG2 [Grade Group 2]) at diagnostic biopsy based on multiparametric prostate MRI (mpMRI), serum biomarkers, and patient clinicodemographic features.

Materials and methods

Nomograms were developed from a cohort of biopsy-naïve men presenting to our 11-hospital system with prostate specific antigen (PSA) of 2–20 ng/mL who underwent pre-biopsy mpMRI from March 2018-June 2021 (n = 1494). The outcomes were the presence of csPCa and high-grade prostate cancer (defined as ≥GG3 prostate cancer). Using significant variables on multivariable logistic regression, individual nomograms were developed for men with total PSA, % free PSA, or prostate health index (PHI) when available. The nomograms were both internally validated and evaluated in an independent cohort of 366 men presenting to our hospital system from July 2021-February 2022.

Results

1031 of 1494 men (69%) underwent biopsy after initial evaluation with mpMRI, 493 (47.8%) of whom were found to have ≥GG2 PCa, and 271 (26.3%) were found to have ≥GG3 PCa. Age, race, highest PIRADS score, prostate health index when available, % free PSA when available, and PSA density were significant predictors of ≥GG2 and ≥GG3 PCa on multivariable analysis and were used for nomogram generation. Accuracy of nomograms in both the training cohort and independent cohort were high, with areas under the curves (AUC) of ≥0.885 in the training cohort and ≥0.896 in the independent validation cohort. In our independent validation cohort, our model for ≥GG2 prostate cancer with PHI saved 39.1% of biopsies (143/366) while only missing 0.8% of csPCa (1/124) with a biopsy threshold of 20% probability of csPCa.

Conclusions

Here we developed nomograms combining serum testing and mpMRI to help clinicians risk stratify patients with elevated PSA of 2–20 ng/mL who are being considered for biopsy. Our nomograms are available at https://rossnm1.shinyapps.io/MynMRIskCalculator/ to aid with biopsy decisions.

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Fig. 1: My nMRIsk Prostate Cancer Nomograms using PHI.
Fig. 2: Biopsies saved for PHI with GG2 and GG3 Pca in development and validation cohorts.

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Data availability

Data may be available for review and secondary analysis upon request as per institutional guidelines and policies.

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Funding

Funding

Urology Care Foundation: Robert J Krane, MD Resident Research Award.

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Author notes

  1. These authors contributed equally: Mohammad R. Siddiqui, Eric V. Li.

Authors and Affiliations

  1. Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Mohammad R. Siddiqui, Eric V. Li, Sai K. S. R. Kumar, Anna Busza, Jasmine S. Lin, Ashorne K. Mahenthiran, Jonathan A. Aguiar, Parth V. Shah, Brandon Ansbro, Jordan M. Rich, Mary-Kate Keeter, Quan Mai, Edward M. Schaeffer, Hiten D. Patel & Ashley E. Ross

  2. Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Soliman A. S. Moataz

  3. Department of Preventative Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Xinlei Mi

  4. Department of Urology, Vanderbilt University, Nashville, TN, USA

    Jeffrey J. Tosoian

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  1. Mohammad R. Siddiqui
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Contributions

MRS – patient record review, data analysis, manuscript preparation. EVL - patient record review, data analysis, manuscript preparation. SKSRK – Biostatstics. AB – patient record review. JSL – patient record review. AKM – patient record review, manuscript preparation. JAA – patient record review, data analysis. PVS – patient record review, manuscript preparation. BA – patient record review, data analytics. JMR – patient record review, data cleaning. SASM – data analytics. MKK – data support QM – data analytics support. XM – biostatistics. JJT – mentorship and guidance with the project. EMS – mentorship and guidance HDP – mentorship, guidance, and manuscript preparation. AER – mentorship, guidance with all aspects of the project.

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Correspondence to Mohammad R. Siddiqui.

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Siddiqui, M.R., Li, E.V., Kumar, S.K.S.R. et al. Optimizing detection of clinically significant prostate cancer through nomograms incorporating mri, clinical features, and advanced serum biomarkers in biopsy naïve men. Prostate Cancer Prostatic Dis 26, 588–595 (2023). https://doi.org/10.1038/s41391-023-00660-8

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