Jan Philipp Radtke1, Manuel Wiesenfarth2, Claudia Kesch3, Martin T Freitag4, Celine D Alt5, Kamil Celik3, Florian Distler3, Wilfried Roth6, Kathrin Wieczorek6, Christian Stock7, Stefan Duensing3, Matthias C Roethke4, Dogu Teber3, Heinz-Peter Schlemmer4, Markus Hohenfellner3, David Bonekamp3, Boris A Hadaschik3. 1. Department of Urology, University Hospital Heidelberg, Heidelberg, Germany; Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: j.radtke@dkfz-heidelberg.de. 2. Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany. 3. Department of Urology, University Hospital Heidelberg, Heidelberg, Germany. 4. Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. 5. Department of Diagnostic and Interventional Radiology, University Düsseldorf, Medical Faculty, Düsseldorf, Germany. 6. Institute of Pathology, University of Heidelberg, Heidelberg, Germany. 7. Institute of Medical Biometry and Informatics, University Hospital Heidelberg, Heidelberg, Germany.
Abstract
BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) is gaining widespread acceptance in prostate cancer (PC) diagnosis and improves significant PC (sPC; Gleason score≥3+4) detection. Decision making based on European Randomised Study of Screening for PC (ERSPC) risk-calculator (RC) parameters may overcome prostate-specific antigen (PSA) limitations. OBJECTIVE: We added pre-biopsy mpMRI to ERSPC-RC parameters and developed risk models (RMs) to predict individual sPC risk for biopsy-naïve men and men after previous biopsy. DESIGN, SETTING, AND PARTICIPANTS: We retrospectively analyzed clinical parameters of 1159 men who underwent mpMRI prior to MRI/transrectal ultrasound fusion biopsy between 2012 and 2015. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariate regression analyses were used to determine significant sPC predictors for RM development. The prediction performance was compared with ERSPC-RCs, RCs refitted on our cohort, Prostate Imaging Reporting and Data System (PI-RADS) v1.0, and ERSPC-RC plus PI-RADSv1.0 using receiver-operating characteristics (ROCs). Discrimination and calibration of the RM, as well as net decision and reduction curve analyses were evaluated based on resampling methods. RESULTS AND LIMITATIONS: PSA, prostate volume, digital-rectal examination, and PI-RADS were significant sPC predictors and included in the RMs together with age. The ROC area under the curve of the RM for biopsy-naïve men was comparable with ERSPC-RC3 plus PI-RADSv1.0 (0.83 vs 0.84) but larger compared with ERSPC-RC3 (0.81), refitted RC3 (0.80), and PI-RADS (0.76). For postbiopsy men, the novel RM's discrimination (0.81) was higher, compared with PI-RADS (0.78), ERSPC-RC4 (0.66), refitted RC4 (0.76), and ERSPC-RC4 plus PI-RADSv1.0 (0.78). Both RM benefits exceeded those of ERSPC-RCs and PI-RADS in the decision regarding which patient to receive biopsy and enabled the highest reduction rate of unnecessary biopsies. Limitations include a monocentric design and a lack of PI-RADSv2.0. CONCLUSIONS: The novel RMs, incorporating clinical parameters and PI-RADS, performed significantly better compared with RMs without PI-RADS and provided measurable benefit in making the decision to biopsy men at a suspicion of PC. For biopsy-naïve patients, both our RM and ERSPC-RC3 plus PI-RADSv1.0 exceeded the prediction performance compared with clinical parameters alone. PATIENT SUMMARY: Combined risk models including clinical and imaging parameters predict clinically relevant prostate cancer significantly better than clinical risk calculators and multiparametric magnetic resonance imaging alone. The risk models demonstrate a benefit in making a decision about which patient needs a biopsy and concurrently help avoid unnecessary biopsies.
BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) is gaining widespread acceptance in prostate cancer (PC) diagnosis and improves significant PC (sPC; Gleason score≥3+4) detection. Decision making based on European Randomised Study of Screening for PC (ERSPC) risk-calculator (RC) parameters may overcome prostate-specific antigen (PSA) limitations. OBJECTIVE: We added pre-biopsy mpMRI to ERSPC-RC parameters and developed risk models (RMs) to predict individual sPC risk for biopsy-naïve men and men after previous biopsy. DESIGN, SETTING, AND PARTICIPANTS: We retrospectively analyzed clinical parameters of 1159 men who underwent mpMRI prior to MRI/transrectal ultrasound fusion biopsy between 2012 and 2015. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Multivariate regression analyses were used to determine significant sPC predictors for RM development. The prediction performance was compared with ERSPC-RCs, RCs refitted on our cohort, Prostate Imaging Reporting and Data System (PI-RADS) v1.0, and ERSPC-RC plus PI-RADSv1.0 using receiver-operating characteristics (ROCs). Discrimination and calibration of the RM, as well as net decision and reduction curve analyses were evaluated based on resampling methods. RESULTS AND LIMITATIONS: PSA, prostate volume, digital-rectal examination, and PI-RADS were significant sPC predictors and included in the RMs together with age. The ROC area under the curve of the RM for biopsy-naïve men was comparable with ERSPC-RC3 plus PI-RADSv1.0 (0.83 vs 0.84) but larger compared with ERSPC-RC3 (0.81), refitted RC3 (0.80), and PI-RADS (0.76). For postbiopsy men, the novel RM's discrimination (0.81) was higher, compared with PI-RADS (0.78), ERSPC-RC4 (0.66), refitted RC4 (0.76), and ERSPC-RC4 plus PI-RADSv1.0 (0.78). Both RM benefits exceeded those of ERSPC-RCs and PI-RADS in the decision regarding which patient to receive biopsy and enabled the highest reduction rate of unnecessary biopsies. Limitations include a monocentric design and a lack of PI-RADSv2.0. CONCLUSIONS: The novel RMs, incorporating clinical parameters and PI-RADS, performed significantly better compared with RMs without PI-RADS and provided measurable benefit in making the decision to biopsy men at a suspicion of PC. For biopsy-naïve patients, both our RM and ERSPC-RC3 plus PI-RADSv1.0 exceeded the prediction performance compared with clinical parameters alone. PATIENT SUMMARY: Combined risk models including clinical and imaging parameters predict clinically relevant prostate cancer significantly better than clinical risk calculators and multiparametric magnetic resonance imaging alone. The risk models demonstrate a benefit in making a decision about which patient needs a biopsy and concurrently help avoid unnecessary biopsies.
Keywords:
European Randomised Study of Screening for Prostate Cancer; Magnetic resonance imaging; Multiparametric magnetic resonance imaging; Prostate cancer; Risk model; Risk stratification
Authors: David Bonekamp; M B Wolf; M C Roethke; S Pahernik; B A Hadaschik; G Hatiboglu; T H Kuru; I V Popeneciu; J L Chin; M Billia; J Relle; J Hafron; K R Nandalur; R M Staruch; M Burtnyk; M Hohenfellner; H-P Schlemmer Journal: Eur Radiol Date: 2018-06-25 Impact factor: 5.315
Authors: Anwar R Padhani; Jelle Barentsz; Geert Villeirs; Andrew B Rosenkrantz; Daniel J Margolis; Baris Turkbey; Harriet C Thoeny; François Cornud; Masoom A Haider; Katarzyna J Macura; Clare M Tempany; Sadhna Verma; Jeffrey C Weinreb Journal: Radiology Date: 2019-06-11 Impact factor: 11.105
Authors: Frank-Jan H Drost; Daniël F Osses; Daan Nieboer; Ewout W Steyerberg; Chris H Bangma; Monique J Roobol; Ivo G Schoots Journal: Cochrane Database Syst Rev Date: 2019-04-25