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用于辅助疑似前列腺癌患者诊断方法选择的活检规避预测工具的创建与内部验证。

Creation and internal validation of a biopsy avoidance prediction tool to aid in the choice of diagnostic approach in patients with prostate cancer suspicion.

作者信息

Bhindi Bimal, Jiang Haiyan, Poyet Cedric, Hermanns Thomas, Hamilton Robert J, Li Kathy, Toi Ants, Finelli Antonio, Zlotta Alexandre R, van der Kwast Theodorus H, Evans Andrew, Fleshner Neil E, Kulkarni Girish S

机构信息

Department of Surgery, University Health Network, University of Toronto, Toronto, Canada; Department of Urology, Mayo Clinic, Rochester, MN.

Department of Biostatistics, University Health Network, University of Toronto, Toronto, Canada.

出版信息

Urol Oncol. 2017 Oct;35(10):604.e17-604.e24. doi: 10.1016/j.urolonc.2017.06.044. Epub 2017 Aug 7.

Abstract

INTRODUCTION

To reduce unnecessary prostate biopsies while using novel tests judiciously, we created a tool to predict the probability of clinically significant prostate cancer (CSPC) vs. low-risk prostate cancer or negative biopsy (i.e., when intervention is likely not needed) among men undergoing initial or repeat biopsy.

METHODS

Separate models were created for men undergoing initial and repeat biopsy, identified from our institutional biopsy database and the placebo arm of the REDUCE trial, respectively, to predict the presence of CSPC (Gleason≥7 or>33% of cores involved). Predictors considered included age, race, body mass index, family history of prostate cancer, digital rectal examination, prostate volume, prostate-specific antigen (PSA), free-to-total PSA, presence of high-grade prostatic intraepithelial neoplasia or atypical small acinar proliferation on prior biopsy, number of prior biopsies, and number of cores previously taken. Multivariable logistic regression models that minimized the Akaike Information Criterion and maximized out-of-sample area under the receiver operating characteristics curve (AUC) were selected.

RESULTS

Of 7,963 biopsies (initial = 2,042; repeat = 5,921), 1,138 had CSPC (initial = 870 [42.6%]; repeat = 268 [4.5%]). Age, race, body mass index, family history, digital rectal examination, and PSA were included in the initial biopsy model (out-of-sample AUC = 0.74). Age, prostate volume, PSA, free-to-total PSA, prior high-grade prostatic intraepithelial neoplasia, and number of prior biopsies were included in the repeat biopsy model (out-of-sample AUC = 0.81).

CONCLUSION

These prediction models may help guide clinicians in avoiding unnecessary initial and repeat biopsies in men unlikely to harbor CSPC. This tool may also allow for the more judicious use of novel tests only in patients in need of further risk stratification before deciding whether to biopsy.

摘要

引言

为了在合理使用新型检测方法的同时减少不必要的前列腺活检,我们创建了一种工具,用于预测初次或重复活检男性中临床显著前列腺癌(CSPC)与低风险前列腺癌或活检阴性(即可能不需要干预)的概率。

方法

分别为初次活检和重复活检的男性创建模型,分别从我们机构的活检数据库和REDUCE试验的安慰剂组中识别出这些男性,以预测CSPC的存在(Gleason评分≥7或累及的核心超过33%)。考虑的预测因素包括年龄、种族、体重指数、前列腺癌家族史、直肠指检、前列腺体积、前列腺特异性抗原(PSA)、游离PSA与总PSA的比值、先前活检中高级别前列腺上皮内瘤变或非典型小腺泡增生的存在、先前活检的次数以及先前采集的核心数量。选择使赤池信息准则最小化并使样本外受试者工作特征曲线下面积(AUC)最大化的多变量逻辑回归模型。

结果

在7963次活检中(初次活检 = 2042次;重复活检 = 5921次),1138次有CSPC(初次活检 = 870次[42.6%];重复活检 = 268次[4.5%])。初次活检模型纳入了年龄、种族、体重指数、家族史、直肠指检和PSA(样本外AUC = 0.74)。重复活检模型纳入了年龄、前列腺体积、PSA、游离PSA与总PSA的比值、先前的高级别前列腺上皮内瘤变以及先前活检的次数(样本外AUC = 0.81)。

结论

这些预测模型可能有助于指导临床医生避免对不太可能患有CSPC的男性进行不必要的初次和重复活检。该工具还可以仅在需要进一步风险分层以决定是否进行活检的患者中更明智地使用新型检测方法。

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