Multivariate risk prediction tools including MRI for individualized biopsy decision in prostate cancer diagnosis: current status and future directions

Individualized risk-adapted algorithms in prostate cancer (PCa) diagnosis using predictive prebiopsy variables in addition to prostate-specific antigen value may result in a considerable reduction of unnecessary systematic biopsies. Multi-parametric magnetic resonance imaging (mpMRI) has emerged as a secondary prediction tool that can further improve the detection of clinically significant prostate cancer (csPCa). This review explores the performance of new MRI risk models for indicating a biopsy for prostate cancer diagnosis.

The area under the receiver-operating characteristic curve for detecting csPCa varies between 0.64 and 0.91 in biopsy-naïve men, and between 0.78 and 0.93 in men with a previous negative biopsy. The utility of multivariate risk prediction tools including MRI suspicion scores as an extra input parameter has the potential to avoid a notable number of biopsies and detection of clinically insignificant PCa at a low price of missing some csPCa. The trade-off depends on the risk threshold that is chosen. In biopsy-naïve men a net benefit was obtained at a risk threshold of above 10% for csPCa in most MRI risk prediction models. All constructed MRI risk models used (referral) patient cohorts with high prevalence of csPCa. Using more representative cohorts from daily clinical screening, net benefit may attenuate at lower risk thresholds. Strengths and limitations of these models are discussed.

To assess their wider applicability, in-depth analysis of mpMRI predictive qualities should be further investigated, in combination with required external validation of these models in a multicenter setting with large prospective datasets.