Abstract：
OBJECTIVE: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa).
METHODS: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%-20%.
RESULTS: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63-72) years, 9 (7-15) ng/mL, and 0.20 (0.13-0.32) ng/mL2 , respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD.
CONCLUSIONS: Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.
METHODS: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%-20%.
RESULTS: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63-72) years, 9 (7-15) ng/mL, and 0.20 (0.13-0.32) ng/mL2 , respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD.
CONCLUSIONS: Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.
摘要：
目的：比较目前可用的结合磁共振成像（MRI）发现的活检决策支持工具在预测有临床意义的前列腺癌（csPCa）中的性能。
方法：我们回顾性地纳入了在两个大型欧洲中心接受前列腺MRI和随后的靶向和/或系统性前列腺活检的男性。通过PubMed搜索确定了可用的决策支持工具。通过校准评估性能，歧视,决策曲线分析（DCA）和避免的活检数量与CSPCa漏诊,在重新校准之前和之后，风险阈值5-20%。
结果：包含940名男性，507（54%）患有csPCa。年龄的中位数和四分位数范围，PSA,PSAD为68(63-72)年，9（7-15）ng/ml，和0.20(0.13-0.32)ng/ml2，分别。评估了18个多变量风险计算器（MRI-RC）和基于MRI发现和前列腺特异性抗原密度阈值（PSAD）的二分法活检决策策略。VanLeeuwen模型和鹿特丹前列腺癌风险计算器（RPCRC）具有MRI-RC的最佳辨别能力（AUC0.86），可以在整个队列中进行评估。DCA对VanLeeuwen模型显示出最高的临床效用，其次是RPCRC。在10％的阈值下，VanLeeuwen模型将避免22％的活检，缺少1.8%的CSPCa，虽然RPCRC会避免20%的活检，缺少2.6%的csPCa。这些多变量模型优于仅基于MRI发现和PSAD的所有二分决策策略。
结论：即使在这个高风险队列中，活检决策支持工具将避免许多前列腺活检，虽然很少遗漏csPCa病例。VanLeeuwen模型具有最高的临床效用，其次是RPCRC。这些多变量MRI-RC优于仅基于MRI和PSAD的决策策略。
方法：我们回顾性地纳入了在两个大型欧洲中心接受前列腺MRI和随后的靶向和/或系统性前列腺活检的男性。通过PubMed搜索确定了可用的决策支持工具。通过校准评估性能，歧视,决策曲线分析（DCA）和避免的活检数量与CSPCa漏诊,在重新校准之前和之后，风险阈值5-20%。
结果：包含940名男性，507（54%）患有csPCa。年龄的中位数和四分位数范围，PSA,PSAD为68(63-72)年，9（7-15）ng/ml，和0.20(0.13-0.32)ng/ml2，分别。评估了18个多变量风险计算器（MRI-RC）和基于MRI发现和前列腺特异性抗原密度阈值（PSAD）的二分法活检决策策略。VanLeeuwen模型和鹿特丹前列腺癌风险计算器（RPCRC）具有MRI-RC的最佳辨别能力（AUC0.86），可以在整个队列中进行评估。DCA对VanLeeuwen模型显示出最高的临床效用，其次是RPCRC。在10％的阈值下，VanLeeuwen模型将避免22％的活检，缺少1.8%的CSPCa，虽然RPCRC会避免20%的活检，缺少2.6%的csPCa。这些多变量模型优于仅基于MRI发现和PSAD的所有二分决策策略。
结论：即使在这个高风险队列中，活检决策支持工具将避免许多前列腺活检，虽然很少遗漏csPCa病例。VanLeeuwen模型具有最高的临床效用，其次是RPCRC。这些多变量MRI-RC优于仅基于MRI和PSAD的决策策略。
