A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population

Noh, Tae Il; Hyun, Chang Wan; Kang, Ha Eun; Jin, Hyun Jung; Tae, Jong Hyun; Shim, Ji Sung; Kang, Sung Gu; Sung, Deuk Jae; Cheon, Jun; Lee, Jeong Gu; Kang, Seok Ho

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Cancer Research and Treatment > Accepted Articles

Original Article

doi: https://doi.org/10.4143/crt.2020.1068 [Accepted]

A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population

Tae Il Noh¹, Chang Wan Hyun¹, Ha Eun Kang¹, Hyun Jung Jin¹, Jong Hyun Tae¹, Ji Sung Shim¹, Sung Gu Kang¹, Deuk Jae Sung^1,2, Jun Cheon¹, Jeong Gu Lee¹, Seok Ho Kang¹

¹Department of Urology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
²Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea

Correspondence

Seok Ho Kang ,Tel: 82-2-920-5530, Fax: 82-2-928-7864, Email: mdksh@korea.ac.kr

Received: October 18, 2020; Accepted: December 31, 2020. Published online: December 31, 2020.

ABSTRACT

Purpose
This study aimed to develop and validate a predictive model for the assessment of clinically significant prostate cancer (csPCa) in men, prior to prostate biopsies, based on bi-parametric magnetic resonance imaging (bpMRI) and clinical parameters.
Materials and Methods
We retrospectively analyzed 300 men with clinical suspicion of prostate cancer (prostate-specific antigen [PSA] ≥ 4.0 ng/mL and/or abnormal findings in a digital rectal examination [DRE]), who underwent bpMRI-ultrasound fusion transperineal targeted and systematic biopsies (bpMRI-US transperineal FTSB) in the same session, at a Korean university hospital. Predictive models, based on Prostate Imaging Reporting and Data Systems (PI-RADS) scores of bpMRI and clinical parameters, were developed to detect csPCa (intermediate/high grade [GS ≥ 3 + 4]) and compared by analyzing the areas under the curves and decision curves.
Results
A predictive model defined by the combination of bpMRI and clinical parameters (age, PSA density) showed high discriminatory power (area under the curve, 0.861) and resulted in a significant net benefit on decision curve analysis. Applying a probability threshold of 7.5%, 21.6% of men could avoid unnecessary prostate biopsy, while only 1.0% of significant prostate cancers were missed.
Conclusion
This predictive model provided a reliable and measurable means of risk stratification of csPCa, with high discriminatory power and great net benefit. It could be a useful tool for clinical decision-making prior to prostate biopsies.

Key words: Prostatic neoplasms, Bi-parametric MRI, Transperineal prostate biopsy, Nomograms