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. 2016:2016:7105678.
doi: 10.1155/2016/7105678. Epub 2016 Apr 11.

Predicting Low-Risk Prostate Cancer from Transperineal Saturation Biopsies

Affiliations

Predicting Low-Risk Prostate Cancer from Transperineal Saturation Biopsies

Pim J van Leeuwen et al. Prostate Cancer. 2016.

Abstract

Introduction. To assess the performance of five previously described clinicopathological definitions of low-risk prostate cancer (PC). Materials and Methods. Men who underwent radical prostatectomy (RP) for clinical stage ≤T2, PSA <10 ng/mL, Gleason score <8 PC, diagnosed by transperineal template-guided saturation biopsy were included. The performance of five previously described criteria (i.e., criteria 1-5, criterion 1 stringent (Gleason score 6 + ≤5 mm total max core length PC + ≤3 mm max per core length PC) up to criterion 5 less stringent (Gleason score 6-7 with ≤5% Gleason grade 4) was analysed to assess ability of each to predict insignificant disease in RP specimens (defined as Gleason score ≤6 and total tumour volume <2.5 mL, or Gleason score 7 with ≤5% grade 4 and total tumour volume <0.7 mL). Results. 994 men who underwent RP were included. Criterion 4 (Gleason score 6) performed best with area under the curve of receiver operating characteristics 0.792. At decision curve analysis, criterion 4 was deemed clinically the best performing transperineal saturation biopsy-based definition for low-risk PC. Conclusions. Gleason score 6 disease demonstrated a superior trade-off between sensitivity and specificity for clarifying low-risk PC that can guide treatment and be used as reference test in diagnostic studies.

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Figures

Figure 1
Figure 1
Decision curve analysis, demonstrating the net benefit, as measured by rate of nontreating men for low-risk prostate cancer, using the five decision-making strategies as listed in the legend. Threshold probability is the threshold probability of low-risk PC at which an individual considers the benefit of treatment for PC equivalent to the harm of overtreatment for low-risk disease, and thus it reflects how the individual weights the benefits and harms associated with this decision. The highest curve at any given threshold probability is the optimal decision-making strategy to maximize net benefit.

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