Wang Siao-Yi, Cowan Janet E, Cary K Clint, Chan June M, Carroll Peter R, Cooperberg Matthew R
Department of Urology, University of California, Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA.
BJU Int. 2014 Dec;114(6b):E18-E24. doi: 10.1111/bju.12554. Epub 2014 Feb 20.
To assess the ability of current nomograms to predict disease progression at repeat biopsy or at delayed radical prostatectomy (RP) in a prospectively accrued cohort of patients managed by active surveillance (AS).
A total of 273 patients meeting low-risk criteria who were managed by AS and who underwent multiple biopsies and/or delayed RP were included in the study. The Kattan (base, medium and full), Steyerberg, Nakanishi and Chun nomograms were used to calculate the likelihood of indolent disease ('nomogram probability') as well as to predict 'biopsy progression' by grade or volume, 'surgical progression' by grade or stage, or 'any progression' on repeat biopsy or surgery. We evaluated the associations between each nomogram probability and each progression outcome using logistic regression with (area under the receiver-operating characteristic curve (AUC) values and decision curve analysis.
The nomogram probabilities of indolent disease were lower in patients with biopsy progression (P < 0.01) and any progression on repeat biopsy or surgical pathology (P < 0.05). In regression analyses, nomograms showed a modest ability to predict biopsy progression, adjusted for total number of biopsies (AUC range 0.52-0.67) and any progression (AUC range 0.52-0.70). Decision curve analyses showed that all the nomograms, except for the Kattan base model, have similar value in predicting biopsy progression and any progression. Nomogram probabilities were not associated with surgical progression in a subgroup of 58 men who underwent delayed RP.
Existing nomograms have only modest accuracy in predicting the outcomes of patients undergoing AS. Improvements to existing nomograms should be made before they are implemented in clinical practice and used to select patients for AS.
评估当前列线图在一个前瞻性纳入的主动监测（AS）管理的患者队列中，预测重复活检时或延迟根治性前列腺切除术（RP）时疾病进展的能力。
本研究纳入了273例符合低风险标准、接受AS管理且接受多次活检和/或延迟RP的患者。使用卡坦（基础、中等和完整）、斯泰尔伯格、中岸和春列线图来计算惰性疾病的可能性（“列线图概率”），以及按分级或体积预测“活检进展”、按分级或分期预测“手术进展”，或预测重复活检或手术时的“任何进展”。我们使用逻辑回归（受试者操作特征曲线下面积（AUC）值和决策曲线分析）评估每个列线图概率与每个进展结果之间的关联。
活检进展的患者（P < 0.01）以及重复活检或手术病理有任何进展的患者（P < 0.05），其惰性疾病的列线图概率较低。在回归分析中，列线图显示出在调整活检总数后预测活检进展的适度能力（AUC范围为0.52 - 0.67）和任何进展（AUC范围为0.52 - 0.70）。决策曲线分析表明，除卡坦基础模型外，所有列线图在预测活检进展和任何进展方面具有相似的价值。在接受延迟RP的58名男性亚组中，列线图概率与手术进展无关。
现有列线图在预测接受AS的患者结局方面准确性仅为中等。在将现有列线图应用于临床实践并用于选择AS患者之前，应进行改进。
