Cancer Res Treat.  2021 Oct;53(4):1148-1155. 10.4143/crt.2020.1068.

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

• Noh TI
• Hyun CW
• Kang HE
• Jin HJ
• Tae JH
• Shim JS
• Kang SG
• Sung DJ
• Cheon J
• Lee JG
• Kang SH

• KMID: 2521589
• DOI: http://doi.org/10.4143/crt.2020.1068

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), who underwent bpMRI-ultrasound fusion transperineal targeted and systematic biopsies in the same session, at a Korean university hospital. Predictive models, based on Prostate Imaging Reporting and Data Systems scores of bpMRI and clinical parameters, were developed to detect csPCa (intermediate/high grade [Gleason score ≥ 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.

Keyword

Prostatic neoplasms; Bi-parametric magnetic resonance imaging; Transperineal prostate biopsy; Nomograms

Figure

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  Fig. 1 Nomogram of the predictive model for the probability of clinically significant prostate cancer (Gleason score ≥ 7 [3+4]). PI-RADS, Prostate Imaging Reporting and Data System; PSA, prostate-specific antigen.

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  Fig. 2 Internal and external validation. Hosmer-Lemeshow good-ness-of-fit test: Internal set: p=0.324, External set: p=0.303.

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  Fig. 3 Net benefit decision curve. Net benefit=Benefit–(Harm× Exchange rate). The value excluding the false-positive rate from the true-positive rate of cancer, based on the high-risk threshold in the probability values estimated from the model. A net benefit of 20% means that the marker is equivalent to a strategy that led to biopsy in 20 men per 100 men at risk, with all biopsy results positive for cancer. “All” is the net benefit when all individuals are biopsied, and if it is greater than this value, pure true-positive minus harm is greater than the biopsy of all individuals. MRI, magnetic resonance imaging; PSAD, prostate-specific antigen density.

Reference

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