Combined clinical parameters and multiparametric MRI for advanced risk modeling of prostate cancer - patient-tailored risk stratification can reduce unnecessary biopsies

32nd Annual EAU Congress, 24-28 March 2017, London, United Kingdom

498

Combined clinical parameters and multiparametric MRI for advanced risk modeling of prostate cancer - patient-tailored risk stratification can reduce unnecessary biopsies Eur Urol Suppl 2017; 16(3);e861

Radtke J.P.1, Bonekamp D.2, Kesch C.1, Freitag M.2, Alt C.3, Celik K.1, Distler F.4, Roth W.5, Wieczorek K.6, Duensing S.1, Roethke M.2, Teber D.1, Schlemmer H-P.2, Hohenfellner M.1, Hadaschik B.1 1

University Hospital Heidelberg, Dept. of Urology, Heidelberg, Germany, 2German Cancer Research Center, Dept. of Radiology, Heidelberg, Germany, 3University Hospital Düsseldorf, Dept. of Interventional and Diagnostic Radiology, Düsseldorf, Germany, 4Paracelsus University Nürnberg, Dept. of Urology, Nürnberg, Germany, 5University Medicine Mainz, Dept. of Pathology, Mainz, Germany, 6 University of Heidelberg, Dept. of Pathology, Heidelberg, Germany INTRODUCTION & OBJECTIVES: Multiparametric MRI (mpMRI) is gaining widespread acceptance in prostate cancer (PC) diagnosis and improves significant PC (sPC) detection (Gleason-score ≥3+4). Decision making based on European Randomised study of Screening for PC (ERSPC) risk-calculator (RC) parameters may overcome PSA-screening limitations. We added pre-biopsy mpMRI to ERSPC-RC parameters and developed a risk model (RM) to predict individual sPC-risk on biopsy. MATERIAL & METHODS: We retrospectively analyzed clinical parameters of 755 men (biopsy-naïve or after previous biopsy) who underwent mpMRI prior to MRI/TRUS-fusion-biopsy between 2012 and 2014 as training sample. The RM was validated in 404 consecutive patients in 2015. A stepwise multivariate regression analysis was used to determine significant sPC-predictors in the training set and to develop the RM. The accuracy was compared to ERSPC-RC3 (for biopsy-naïve men) and 4 (for patients after previous biopsy) and PI-RADSv1.0 scoring using receiver operating characteristics (ROC). We also recalibrated ERSPC-RC3 and 4 for our cohort and compared the results to our RM. Discrimination and calibration of the RM, as well as net decision and reduction curve analyses were evaluated in validation set. RESULTS: PSA, prostate volume, digital-rectal examination and PI-RADS were significant sPCpredictors and included in the RM (Figure e). ROC area under the curve (AUC) for the RM was significantly larger (0.82 each), compared to ERSPC-RC3 (0.79, p=0.004), RC4 (0.68, p<0.001) and PIRADS (0.74-76, p=0.015 and p=0.006)(Figure a-d). Similarly, in the validation cohort, RM`s discrimination was higher for biopsy-naïve and post-biopsy men (0.84 and 0.76), compared to PI-RADS (0.76 and 0.69, p=0.002 and p=0.006) and ERSPC-RC3/4 (0.79/0.74, p=0.003/p=0.146). The AUC of the RM also exceeded those of both recalibrated ERSPC-RCs (Figure a-d). The calibration plot demonstrated an excellent slope (1.03)(Figure f). The RM`s benefit exceeded that of ERSPC-RCs and PI-RADS in the decision which patient to biopsy and enabled the highest reduction rate of unnecessary biopsies. CONCLUSIONS: The novel RM, incorporating ERSPC-RC parameters and PI-RADS, performed significantly better compared to the tools alone and provides measurable benefit in making the decision

Eur Urol Suppl 2017; 16(3);e861

32nd Annual EAU Congress, 24-28 March 2017, London, United Kingdom

498

Combined clinical parameters and multiparametric MRI for advanced risk modeling of prostate cancer - patient-tailored risk stratification can reduce unnecessary biopsies Eur Urol Suppl 2017; 16(3);e862

to biopsy men at suspicion of PC.

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