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前列腺癌主动监测中的精准医学:金丝雀早期检测研究网络主动监测活检风险计算器的开发。

Precision Medicine in Active Surveillance for Prostate Cancer: Development of the Canary-Early Detection Research Network Active Surveillance Biopsy Risk Calculator.

作者信息

Ankerst Donna P, Xia Jing, Thompson Ian M, Hoefler Josef, Newcomb Lisa F, Brooks James D, Carroll Peter R, Ellis William J, Gleave Martin E, Lance Raymond S, Nelson Peter S, Wagner Andrew A, Wei John T, Etzioni Ruth, Lin Daniel W

机构信息

Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Life Sciences Mathematics Unit, Technische Universitaet Muenchen, Munich, Germany.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

出版信息

Eur Urol. 2015 Dec;68(6):1083-8. doi: 10.1016/j.eururo.2015.03.023. Epub 2015 Mar 25.

Abstract

BACKGROUND

Men on active surveillance (AS) face repeated biopsies. Most biopsy specimens will not show disease progression or change management. Such biopsies do not contribute to patient management and are potentially morbid and costly.

OBJECTIVE

To use a contemporary AS prospective trial to develop a tool to predict AS biopsy outcomes.

DESIGN, SETTING, AND PARTICIPANTS: Biopsy samples (median: 2; range: 2-9 per patient) from 859 men participating in the Canary Prostate Active Surveillance Study and with Gleason 6 prostate cancer (median follow-up: 35.8 mo; range: 3.0-148.7 mo) were analyzed.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Logistic regression was used to predict progression, defined as an increase in Gleason score from ≤6 to ≥7 or increase in percentage of cores positive for cancer from <34% to ≥34%. Fivefold internal cross-validation was performed to evaluate the area under the receiver operating characteristic curve (AUC).

RESULTS AND LIMITATIONS

Statistically significant risk factors for progression on biopsy were prostate-specific antigen (odds ratio [OR]: 1.045; 95% confidence interval [CI], 1.028-1.063), percentage of cores positive for cancer on most recent biopsy (OR: 1.401; 95% CI, 1.301-1.508), and history of at least one prior negative biopsy (OR: 0.524; 95% CI, 0.417-0.659). A multivariable predictive model incorporating these factors plus age and number of months since last biopsy achieved an AUC of 72.4%.

CONCLUSIONS

A combination of readily available clinical measures can stratify patients considering AS prostate biopsy. Risk of progression or upgrade can be estimated and incorporated into clinical practice.

PATIENT SUMMARY

The Canary-Early Detection Research Network Active Surveillance Biopsy Risk Calculator, an online tool, can be used to guide patient decision making regarding follow-up prostate biopsy.

摘要

背景

接受主动监测(AS)的男性面临多次活检。大多数活检标本不会显示疾病进展或改变治疗方案。此类活检对患者治疗并无帮助,且可能带来不良影响并产生高昂费用。

目的

利用一项当代AS前瞻性试验开发一种工具,以预测AS活检结果。

设计、设置和参与者:对参加加那利前列腺主动监测研究且患有Gleason 6级前列腺癌的859名男性的活检样本(中位数:2;范围:每位患者2 - 9个)进行分析(中位随访时间:35.8个月;范围:3.0 - 148.7个月)。

结果测量和统计分析

采用逻辑回归预测疾病进展,定义为Gleason评分从≤6增加到≥7,或癌症阳性核心百分比从<34%增加到≥34%。进行五重内部交叉验证以评估受试者工作特征曲线下面积(AUC)。

结果与局限性

活检时疾病进展的统计学显著危险因素为前列腺特异性抗原(比值比[OR]:1.045;95%置信区间[CI],1.028 - 1.063)、最近一次活检中癌症阳性核心百分比(OR:1.401;95% CI,1.301 - 1.508)以及至少有一次既往阴性活检史(OR:0.524;95% CI,0.417 - 0.659)。纳入这些因素以及年龄和距上次活检的月数的多变量预测模型的AUC为72.4%。

结论

结合易于获得的临床指标可对考虑进行AS前列腺活检的患者进行分层。可估计疾病进展或升级的风险并将其纳入临床实践。

患者总结

加那利早期检测研究网络主动监测活检风险计算器这一在线工具,可用于指导患者关于后续前列腺活检的决策。

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