A predictive model based on biparametric magnetic resonance imaging and clinical parameters for improved risk...

Prostate cancer risk prediction models and multiparametric magnetic resonance imaging (mpMRI) are used for individualised pre-biopsy risk assessment. However, biparametric MRI (bpMRI) has emerged as a simpler, more rapid MRI approach (fewer scan sequences, no intravenous contrast-media) to reduce costs and facilitate a more widespread clinical implementation. It is unknown how bpMRI and risk models perform conjointly. Therefore, the objective was to develop a predictive model for significant prostate cancer (sPCa) in biopsy-naive men based on bpMRI findings and clinical parameters.

Eight hundred and seventy-six biopsy-naive men with clinical suspicion of prostate cancer (prostate-specific antigen, <50 ng/mL; tumour stage, volume, and PSA_density) were created to detect sPCa (any biopsy-core with Gleason grade-group, ≥2) and compared by analysing the areas under the curves and decision curves.

Overall, sPCa was detected in 350/876 men (40%) with median (inter-quartile range) age and PSA level of 65 years (60-70) and 7.3 ng/mL (5.5-10.6), respectively. The model defined by bpMRI scores, age, tumour stage, and PSA_density had the highest discriminatory power (area under the curve, 0.89), showed good calibration on internal bootstrap validation, and resulted in the greatest net benefit on decision curve analysis. Applying a biopsy risk threshold of 20% meant that 42% of men could avoid a biopsy, 50% fewer insignificant cancers were diagnosed, and only 7% of significant cancers (grade-group, ≥2) were missed.

A predictive model based on bpMRI scores and clinical parameters significantly improved risk stratification for sPCa in biopsy-naïve men and could be used for clinical decision-making and counselling men prior to prostate biopsies.