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PLoS One. 2019 Aug 26;14(8):e0221350. doi: 10.1371/journal.pone.0221350. eCollection 2019.

Prediction of significant prostate cancer in biopsy-naïve men: Validation of a novel risk model combining MRI and clinical parameters and comparison to an ERSPC risk calculator and PI-RADS.

Author information

Department of Urology, University Hospital Heidelberg, Heidelberg, Germany.
Department of Radiology, German Cancer Research Center, Heidelberg, Germany.
Department of Radiology, University College London Hospital NHS Foundation Trust, London, United Kingdom.
Division of Surgery & Interventional Science, University College London, London, United Kingdom.
Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany.
Department of Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom.
Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy.
Vita-Salute San Raffaele University, Milan, Italy.
Institute of Pathology, University of Heidelberg, Heidelberg Germany.
Institute of Pathology, University Medicine Mainz, Mainz, Germany.
Department of Pathology, University College Hospital, London, United Kingdom.
Centre for Medical Imaging, University College London Hospitals NHS Foundation Trust, University College London, London, United Kingdom.



Risk models (RM) need external validation to assess their value beyond the setting in which they were developed. We validated a RM combining mpMRI and clinical parameters for the probability of harboring significant prostate cancer (sPC, Gleason Score ≥ 3+4) for biopsy-naïve men.


The original RM was based on data of 670 biopsy-naïve men from Heidelberg University Hospital who underwent mpMRI with PI-RADS scoring prior to MRI/TRUS-fusion biopsy 2012-2015. Validity was tested by a consecutive cohort of biopsy-naïve men from Heidelberg (n = 160) and externally by a cohort of 133 men from University College London Hospital (UCLH). Assessment of validity was performed at fusion-biopsy by calibration plots, receiver operating characteristics curve and decision curve analyses. The RM`s performance was compared to ERSPC-RC3, ERSPC-RC3+PI-RADSv1.0 and PI-RADSv1.0 alone.


SPC was detected in 76 men (48%) at Heidelberg and 38 men (29%) at UCLH. The areas under the curve (AUC) were 0.86 for the RM in both cohorts. For ERSPC-RC3+PI-RADSv1.0 the AUC was 0.84 in Heidelberg and 0.82 at UCLH, for ERSPC-RC3 0.76 at Heidelberg and 0.77 at UCLH and for PI-RADSv1.0 0.79 in Heidelberg and 0.82 at UCLH. Calibration curves suggest that prevalence of sPC needs to be adjusted to local circumstances, as the RM overestimated the risk of harboring sPC in the UCLH cohort. After prevalence-adjustment with respect to the prevalence underlying ERSPC-RC3 to ensure a generalizable comparison, not only between the Heidelberg and die UCLH subgroup, the RM`s Net benefit was superior over the ERSPC`s and the mpMRI`s for threshold probabilities above 0.1 in both cohorts.


The RM discriminated well between men with and without sPC at initial MRI-targeted biopsy but overestimated the sPC-risk at UCLH. Taking prevalence into account, the model demonstrated benefit compared with clinical risk calculators and PI-RADSv1.0 in making the decision to biopsy men at suspicion of PC. However, prevalence differences must be taken into account when using or validating the presented risk model.

Conflict of interest statement

The authors of this manuscript have read the journal's policy and have the following competing interests: DB is a speaker for Profound Medical Inc.. JPR is company consultant for Invivo Uronav, Bender Group, Beckelmann and Saegeling Medizintechnik. FG is funded by the UCL Graduate Research Scholarship and the Brahm PhD scholarship in memory of Chris Adams. ME is a United Kingdom National Institute of Health Research (NIHR) Senior Investigator and receives research support from UCL Hospitals–UCL NIHR Biomedical Research Centre. However this funding is not related to this work and does not alter our adherence to PLOS ONE policies on sharing data and materials.

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