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
Background: 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. Materi...
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| Main Authors: | , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
August 26, 2019
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| In: |
PLOS ONE
Year: 2019, Volume: 14, Issue: 8 |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0221350 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1371/journal.pone.0221350 Verlag: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221350 
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| Author Notes: | Jan Philipp Radtke, Francesco Giganti, Manuel Wiesenfarth, Armando Stabile, Jose Marenco, Clement Orczyk, Veeru Kasivisvanathan, Joanne Nyaboe Nyarangi-Dix, Viktoria Schütz, Svenja Dieffenbacher, Magdalena Görtz, Albrecht Stenzinger, Wilfried Roth, Alex Freeman, Shonit Punwani, David Bonekamp, Heinz-Peter Schlemmer, Markus Hohenfellner, Mark Emberton, Caroline M. Moore |
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| 245 | 1 | 0 | |a Prediction of significant prostate cancer in biopsy-naïve men |b Validation of a novel risk model combining MRI and clinical parameters and comparison to an ERSPC risk calculator and PI-RADS |c Jan Philipp Radtke, Francesco Giganti, Manuel Wiesenfarth, Armando Stabile, Jose Marenco, Clement Orczyk, Veeru Kasivisvanathan, Joanne Nyaboe Nyarangi-Dix, Viktoria Schütz, Svenja Dieffenbacher, Magdalena Görtz, Albrecht Stenzinger, Wilfried Roth, Alex Freeman, Shonit Punwani, David Bonekamp, Heinz-Peter Schlemmer, Markus Hohenfellner, Mark Emberton, Caroline M. Moore |
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| 520 | |a Background: 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. Material and methods: 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. Results: 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. Conclusions: 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. | ||
| 650 | 4 | |a Biopsy | |
| 650 | 4 | |a Cancer detection and diagnosis | |
| 650 | 4 | |a Forecasting | |
| 650 | 4 | |a Lesions | |
| 650 | 4 | |a Magnetic resonance imaging | |
| 650 | 4 | |a Prostate cancer | |
| 650 | 4 | |a Prostate gland | |
| 650 | 4 | |a Ultrasound imaging | |
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