Mp08-14 Development and Validation of a Nomogram for Predicting Pirads 4-5 Lesions on Multiparametric Prostate Mri

Truong, Matthew; Frye, Thomas; Lam, Dang V.; Park, Ji Hae; Wang, Bokai; Feng, Changyong; Hollenberg, Gary; Weinberg, Eric; Messing, Edward M.

doi:10.1016/j.juro.2017.02.301

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“…Prostate volume was proved to be higher in Chinese compared to western population, which could theoretically lead to a higher PSA value and miss PCa at biopsy [ 14 ]. PSAD, which could eliminate the influence of PV on PSA, was proved to be a significant predictor for PI-RADs 3–5 lesions [ 15 , 16 ]. Also, a recent study [ 17 ] has validated the incremental value of PSAD in combination with PI-RADS for the accuracy of PCa screening and showed that the NPV of PI-RADS could be improved by inclusion of PSAD and unnecessary biopsies could be reduced.…”

Section: Discussionmentioning

confidence: 99%

Development and internal validation of PI-RADs v2-based model for clinically significant prostate cancer

et al. 2018

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BackgroundOur objective is to build a model based on Prostate Imaging Reporting and Data System version 2 (PI-RADs v2) and assess its accuracy by internal validation.MethodsPatients who took prostate biopsy from 2014 to 2015 were retrospectively collected to compose training cohort according to the inclusion criteria and patients in 2016 composing validation cohort. Diagnostic performance was evaluated by analyzing the area under the curve (AUC), calibration curves, and decision curves.ResultsOf the 441 patients involved, the clinically significant prostate cancer (csPCa) detection rate were 40.6% (114/281) and 43.8% (70/160) in the training and validation cohort, respectively. Meanwhile, PCa detection rate were 50.2% (141/281) and 53.8% (86/160). Age, prostate-specific antigen density (PSAD)*10 and PI-RADs v2 score composed the model for PCa (model 1) and csPCa (model 2). The area under the curve of models 1 and 2 was 0.870 (95% CI 0.827–0.912) and 0.753 (95% CI 0.717–0.828) in the training cohort, while 0.845 (95% CI 0.786–0.904) and 0.834 (95% CI 0.787–0.882) in the validation cohort. Both models illustrated good calibration, and decision curve analyses showed good performance in predicting PCa or csPCa when the threshold was 0.35 or above.ConclusionsThe model based on age, PSAD*10 and PI-RADs v2 score showed internally validated high predictive value for both PCa and csPCa. It could be used to improve the diagnostic performance of suspicious PCa.

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Section: Discussionmentioning

confidence: 99%