External validation and comparison of magnetic resonance imaging-based predictive models for clinically significant prostate cancer

Lee, Han Jie; Lee, Alvin; Yang, Xin Yan; Law, Yan Mee; Lau, Weber Kam On; Lee, Lui Shiong; Ho, Henry; Cheng, Christopher; Yuen, J.S.P.; Tay, Kae Jack; Chen, Kenneth

doi:10.1016/j.urolonc.2021.03.003

Cited by 9 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This manuscript is the first external validation study testing RC-R and RC-A in two centers within a "real-life" scenario. Contrary to previously published validation studies [11][12][13][14][15][16][17][18], our validation cohort had significant heterogeneity. We included patients with mpMRIs not performed by dedicated uro-radiologists but multiple radiologists with differing degrees of expertise.…”

Section: Discussionmentioning

confidence: 69%

“…Most recently, various research groups implemented mpMRI findings into the ERSPC-RC. Among these, the RCs of Radtke et al [9] and Alberts et al [10] showed the most promising results in internal and external validation studies [11][12][13][14][15][16][17][18]. As mpMRI quality is highly dependent on the radiologist's experience, among other factors, it is unclear whether these RCs accurately predict the risk of PCa and csPCa when including mpMRI reports not performed by dedicated uro-radiologists.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

External validation of two mpMRI-risk calculators predicting risk of prostate cancer before biopsy

Pallauf

Steinkohl²,

Zimmermann

et al. 2022

World J Urol

View full text Add to dashboard Cite

Purpose Risk calculators (RC) aim to improve prebiopsy risk stratification. Their latest versions now include multiparametric magnetic resonance imaging (mpMRI) findings. For their implementation into clinical practice, critical external validations are needed. Methods We retrospectively analyzed the patient data of 554 men who underwent ultrasound-guided targeted and systematic prostate biopsies at 2 centers. We validated the mpMRI-RCs of Radtke et al. (RC-R) and Alberts et al. (RC-A), previously shown to predict prostate cancer (PCa) and clinically significant PCa (csPCa). We assessed these RCs’ prediction accuracy by analyzing the receiver-operating characteristics (ROC) curve and evaluated their clinical utility using Decision Curve Analysis (DCA), including Net-Benefit and Net-Reduction curves. Results We found that the Area Under the ROC Curve (AUC) for predicting PCa was 0.681 [confidence interval (CI) 95% 0.635–0.727] for RC-A. The AUCs for predicting csPCa were 0.635 (CI 95% 0.583–0.686) for RC-A and 0.676 (CI 95% 0.627–0.725) for RC-R. For example, at a risk threshold of 12%, RC-A needs to assess 334 and RC-R 500 patients to detect one additional true positive PCa or csPCa patient, respectively. At the same risk threshold of 12%, RC-A only needs to assess 6 and RC-R 16 patients to detect one additional true negative PCa or csPCa patient. Conclusion The mpMRI-RCs, RC-R and RC-A, are robust and valuable tools for patient counseling. Although they do not improve PCa and csPCa detection rates by a clinically meaningful margin, they aid in avoiding unnecessary prostate biopsies. Their implementation could reduce overdiagnosis and reduce PCa screening morbidity.

show abstract

Section: Discussionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

External validation of two mpMRI-risk calculators predicting risk of prostate cancer before biopsy

Pallauf

Steinkohl²,

Zimmermann

et al. 2022

World J Urol

View full text Add to dashboard Cite

show abstract

“…Moreover, the basis for the development of the ERSPC RC was European white men (as opposed to a more diverse population at risk for PCa), and the use of biparametric MRI and that of the outdated PI-RADS version 1 protocol are notable limitations. A recent study by Lee et al [22] comparing the current six most efficient RCs showed the RC by van Leeuwen et al [23] to have the greatest effectiveness (AUC 86%), missing only 4% of csPCa at 15% threshold in an Asian cohort. However, similar to many of the abovementioned tools, nonaccessibility (ie, it is not an online web-based tool) limits its clinical utility.…”

Section: Discussionmentioning

confidence: 97%

The Mount Sinai Prebiopsy Risk Calculator for Predicting any Prostate Cancer and Clinically Significant Prostate Cancer: Development of a Risk Predictive Tool and Validation with Advanced Neural Networking, Prostate Magnetic Resonance Imaging Outcome Database, and European Randomized Study of Screening for Prostate Cancer Risk Calculator

Parekh

Ratnani

Falagario

et al. 2022

European Urology Open Science

View full text Add to dashboard Cite

“…A few prior studies have performed comparisons of MRI-based PCa risk models. Each of these studies has generally identified the European-based RPCRC-MRI or Van Leeuwen models as the most promising for clinical use, but the studies predated the publication of the North American PLUM and UCLA-Cornell models.…”

Section: Discussionmentioning

confidence: 99%

“…Püllen et al included 307 European patients and found RPCRC-MRI provided the greatest net benefit among 3 assessed models, but the Van Leeuwen model was not evaluated. Lee et al included 449 Asian patients and found the Van Leeuwen model provided the greatest net benefit among 6 assessed models where RPCRC-MRI was also included. Finally, Saba et al included 468 European patients and found RPCRC-MRI followed by the Van Leeuwen models provided the greatest net benefit at a threshold probability of 15% among 4 assessed MRI-based models.…”

Section: Discussionmentioning

confidence: 99%

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

Patel,

Remmers,

Ellis

et al. 2024

JAMA Netw Open

View full text Add to dashboard Cite

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.

show abstract

External validation and comparison of magnetic resonance imaging-based predictive models for clinically significant prostate cancer

Cited by 9 publications

References 27 publications

External validation of two mpMRI-risk calculators predicting risk of prostate cancer before biopsy

External validation of two mpMRI-risk calculators predicting risk of prostate cancer before biopsy

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

Contact Info

Product

Resources

About