A predictive model based on biparametric magnetic resonance imaging and clinical parameters for improved risk assessment and selection of biopsy-naïve men for prostate biopsies.

Autor: Boesen L; Department of Urology and Urological Research, Herlev Gentofte University Hospital, Herlev Ringvej 75, 2730, Herlev, Denmark. lars.boesen@dadlnet.dk., Thomsen FB; Department of Urology and Urological Research, Herlev Gentofte University Hospital, Herlev Ringvej 75, 2730, Herlev, Denmark., Nørgaard N; Department of Urology and Urological Research, Herlev Gentofte University Hospital, Herlev Ringvej 75, 2730, Herlev, Denmark., Løgager V; Department of Radiology, Herlev Gentofte University Hospital, Herlev Ringvej 75, 2730, Herlev, Denmark., Balslev I; Department of Pathology, Herlev Gentofte University Hospital, Herlev Ringvej 75, 2730, Herlev, Denmark., Bisbjerg R; Department of Urology and Urological Research, Herlev Gentofte University Hospital, Herlev Ringvej 75, 2730, Herlev, Denmark., Thomsen HS; Department of Radiology, Herlev Gentofte University Hospital, Herlev Ringvej 75, 2730, Herlev, Denmark., Jakobsen H; Department of Urology and Urological Research, Herlev Gentofte University Hospital, Herlev Ringvej 75, 2730, Herlev, Denmark.
Jazyk: angličtina
Zdroj: Prostate cancer and prostatic diseases [Prostate Cancer Prostatic Dis] 2019 Dec; Vol. 22 (4), pp. 609-616. Date of Electronic Publication: 2019 Apr 15.
DOI: 10.1038/s41391-019-0149-y
Abstrakt: Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
Databáze: MEDLINE
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