Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
E-pub ahead of print

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

Research output: Contribution to journalJournal articleResearchpeer-review

  1. Radiographic progression with nonrising PSA in metastatic castration-resistant prostate cancer: post hoc analysis of PREVAIL

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. A single-center experience with abiraterone as treatment for metastatic castration-resistant prostate cancer

    Research output: Contribution to journalJournal articleResearchpeer-review

  1. Active Surveillance Versus Radical Prostatectomy in Favorable-risk Localized Prostate Cancer

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Hormone naïve metastatic prostate cancer: How to treat it?

    Research output: Contribution to journalJournal articleResearchpeer-review

View graph of relations

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, <T3) underwent pre-biopsy prostate bpMRI (T2-weighted and diffusion-weighted) followed by 10-core standard biopsies (all men) and MRI-transrectal ultrasound fusion targeted biopsies of bpMRI-suspicious lesions (suspicion score, ≥3). Prediction models based on bpMRI scores and clinical parameters (age, tumour stage, prostate-specific-antigen [PSA] level, prostatevolume, and PSAdensity) 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 PSAdensity 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.

Original languageEnglish
JournalProstate Cancer and Prostatic Diseases
ISSN1365-7852
DOIs
Publication statusE-pub ahead of print - 15 Apr 2019

ID: 57420108