Prediction of High-grade Prostate Cancer Following Multiparametric Magnetic Resonance Imaging: Improving the Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculators

Alberts, Arnout R.; Roobol, Monique J.; Verbeek, Jan; Schoots, Ivo G.; Chiu, Peter Ka‐Fung; Osses, Daniël F.; Tijsterman, Jasper D.; Beerlage, Harrie P.; Mannaerts, Christophe K.; Schimmöller, Lars; Albers, Peter; Arsov, Christian

doi:10.1016/j.eururo.2018.07.031

Cited by 160 publications

(248 citation statements)

References 26 publications

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“…In the MRI-ERSPC-3/4 cohort included in the study by Alberts et al [28], clinically significant PCa prevalence was slightly lower than in the present cohort (36% vs 40%). This may be attributable to the transperineal approach used in the present cohort compared with the transrectal biopsies applied previously.…”

Section: Discussioncontrasting

confidence: 68%

“…Alberts et al showed that, in a cohort of 1000 men, at least 238 and up to 418 biopsies could be saved in biopsy‐naïve men and in men who had a prior negative biopsy, respectively, while missing, at worst, 8% of clinically significant PCas at a threshold of 15%, which was very similar to the present findings in the biopsy‐naïve subgroup, in which 324/1000 biopsies could be saved by missing 37/440 (8%) clinically significant PCas. With 14% (44/319) missed clinically significant PCas and 374/1000 biopsies avoided in the previous negative biopsy cohort, the detection rate in the present cohort was worse than in the original publication . This might be explained by the transperineal systematic biopsy approach used in the present cohort and the relatively small number of patients ( n = 91) in this subgroup.…”

Section: Discussioncontrasting

confidence: 67%

“…All the novel nomograms had high AUCs: ModDis (0.83; 95% CI 0.78–0.87), MRI‐ERSPC‐RC3/4 (0.82; 95% CI 0.78–0.87) and ModRad (0.85; 95% CI 0.81–0.89). Comparing the AUCs of our cohort with the published cohorts of the novel models, we found nearly identical AUCs for MRI‐ERSPC‐RC3/4 (0.82 vs 0.84–0.85), ModDis (0.83 vs 0.79) and ModRad (0.85 vs 0.81–0.83) .…”

Section: Discussionsupporting

confidence: 53%

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External validation of novel magnetic resonance imaging‐based models for prostate cancer prediction

Püllen¹,

Radtke

Wiesenfarth

et al. 2019

BJU International

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Objectives To validate, in an external cohort, three novel risk models, including the recently updated European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator, that combine multiparametric magnetic resonance imaging (mpMRI) and clinical variables to predict clinically significant prostate cancer (PCa). Patients and Methods We retrospectively analysed 307 men who underwent mpMRI prior to transperineal ultrasound fusion biopsy between October 2015 and July 2018 at two German centres. mpMRI was rated by Prostate Imaging Reporting and Data System (PI‐RADS) v2.0 and clinically significant PCa was defined as International Society of Urological Pathology Gleason grade group ≥2. The prediction performance of the three models (MRI‐ERSPC‐3/4, and two risk models published by Radtke et al. and Distler et al., ModRad and ModDis) were compared using receiver‐operating characteristic (ROC) curve analyses, with area under the ROC curve (AUC), calibration curve analyses and decision curves used to assess net benefit. Results The AUCs of the three novel models (MRI‐ERSPC‐3/4, ModRad and ModDis) were 0.82, 0.85 and 0.83, respectively. Calibration curve analyses showed the best intercept for MRI‐ERSPC‐3 and ‐4 of 0.35 and 0.76. Net benefit analyses indicated clear benefit of the MRI‐ERSPC‐3/4 risk models compared with the other two validated models. The MRI‐ERSPC‐3/4 risk models demonstrated a discrimination benefit for a risk threshold of up to 15% for clinically significant PCa as compared to the other risk models. Conclusion In our external validation of three novel prostate cancer risk models, which incorporate mpMRI findings, a head‐to‐head comparison indicated that the MRI‐ERSPC‐3/4 risk model in particular could help to reduce unnecessary biopsies.

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Section: Discussioncontrasting

confidence: 68%

Section: Discussioncontrasting

confidence: 67%

Section: Discussionsupporting

confidence: 53%

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External validation of novel magnetic resonance imaging‐based models for prostate cancer prediction

Püllen¹,

Radtke

Wiesenfarth

et al. 2019

BJU International

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“…When multivariate risk prediction models are used, the calculated risk is a continuum. For clinical practicality, thresholds for performing biopsy need to be defined for multivariate risk prediction tools that include MRI results [15,16,[46][47][48]. Since multivariate risk prediction models incorporating MRI results have not yet been validated, these model thresholds are not yet available in the literature.…”

Section: Combining Multivariable Risk Prediction Tools Including Mrimentioning

confidence: 99%

“…Multivariate risk prediction tools can also be used to decide on the need for prostate MRI by enriching patient selection on one hand and avoiding MRI testing on the other [13,14]. Thereafter, adding anatomical and functional information after MRI to multivariate riskcalculator assessments refines the underlying risk of clinically significant cancer and thereby modulates the need for biopsy, also informing on biopsy methods so as to increase biopsy yields, by identifying targets that are likely to harbor clinically significant disease [15,16].…”

Section: Introductionmentioning

confidence: 99%

Personalizing prostate cancer diagnosis with multivariate risk prediction tools: how should prostate MRI be incorporated?

Schoots

Padhani

2019

World J Urol

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Risk-based patient selection for systematic biopsy in prostate cancer diagnosis has been adopted in daily clinical practice, either by clinical judgment and PSA testing, or using multivariate risk prediction tools. The use of multivariable risk prediction tools can significantly reduce unnecessary systematic biopsies, without compromising the detection of clinically significant disease. Increasingly multi-parametric magnetic resonance imaging (MRI) is performed, not only in men with a persistent suspicion of prostate cancer after prior negative systematic biopsy, but also at initial screening before the first biopsy. The combination of MRI and multivariate risk prediction tools could potentially enhance prostate cancer diagnosis using multivariate MRI incorporated risk-based models to decide on the need for prostate MRI, but also using MRI results to adjusted risk-based models, and to guide MRI-directed biopsies. In this review, we discuss the diagnostic work-up for clinically significant prostate cancer, where the combination of MRI and multivariate risk prediction tools is integrated, and how together they can contribute to personalized diagnosis.Keywords Prostate cancer · Biopsy · Magnetic resonance imaging (MRI) · Risk stratification · Multivariate risk prediction · Risk calculator · NomogramThe following relevant activities related to the submitted work: Ivo G. Schoots is a full panel member of the EAU-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer, and a full panel member of the PI-RADS Steering committee. Anwar R. Padhani is Co-Chair of the PI-RADS Steering committee.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Comparison of Magnetic Resonance Imaging–Based Risk Calculators to Predict Prostate Cancer Risk

Patel,

Remmers,

Ellis

et al. 2024

JAMA Netw Open

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ImportanceMagnetic resonance imaging (MRI)–based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms.ObjectiveTo externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator–MRI [RPCRC-MRI]) in cohorts from Europe and North America.Design, Setting, and ParticipantsThis multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy.InterventionsProstate MRI followed by prostate biopsy.Main Outcomes and MeasuresThe primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated.ResultsA total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort.Conclusions and RelevanceIn this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.

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Prediction of High-grade Prostate Cancer Following Multiparametric Magnetic Resonance Imaging: Improving the Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculators

Cited by 160 publications

References 26 publications

External validation of novel magnetic resonance imaging‐based models for prostate cancer prediction

External validation of novel magnetic resonance imaging‐based models for prostate cancer prediction

Personalizing prostate cancer diagnosis with multivariate risk prediction tools: how should prostate MRI be incorporated?

Comparison of Magnetic Resonance Imaging–Based Risk Calculators to Predict Prostate Cancer Risk

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