Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany; Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada.
Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada; Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany.
Eur Urol Focus. 2022 Sep;8(5):1133-1140. doi: 10.1016/j.euf.2021.07.008. Epub 2021 Jul 30.
Some high-risk prostate cancer (PCa) patients may show more favorable Gleason pattern at radical prostatectomy (RP) than at biopsy.
To test whether downgrading could be predicted accurately.
DESIGN, SETTING, AND PARTICIPANTS: Within the Surveillance, Epidemiology and End Results database (2010-2016), 6690 National Comprehensive Cancer Network (NCCN) high-risk PCa patients were identified.
We randomly split the overall cohort between development and validation cohorts (both n = 3345, 50%). Multivariable logistic regression models used biopsy Gleason, prostate-specific antigen, number of positive prostate biopsy cores, and cT stage to predict downgrading. Accuracy, calibration, and decision curve analysis (DCA) tested the model in the external validation cohort.
Of 6690 patients, 50.3% were downgraded at RP, and of 2315 patients with any biopsy pattern 5, 44.1% were downgraded to RP Gleason pattern ≤4 + 4. Downgrading rates were highest in biopsy Gleason pattern 5 + 5 (84.1%) and lowest in 3 + 4 (4.0%). In the validation cohort, the logistic regression model-derived nomogram predicted downgrading with 71.0% accuracy, with marginal departures (±3.3%) from ideal predictions in calibration. In DCA, a net benefit throughout all threshold probabilities was recorded, relative to treat-all or treat-none strategies and an algorithm based on an average downgrading rate of 50.3%. All steps were repeated in the subgroup with any biopsy Gleason pattern 5, to predict RP Gleason pattern ≤4 + 4. Here, a second nomogram (n = 2315) yielded 68.0% accuracy, maximal departures from ideal prediction of ±5.7%, and virtually the same DCA pattern as the main nomogram.
Downgrading affects half of all high-risk PCa patients. Its presence may be predicted accurately and may help with better treatment planning.
Downgrading occurs in every second high-risk prostate cancer patients. The nomograms developed by us can predict these probabilities accurately.
一些高危前列腺癌（PCa）患者在根治性前列腺切除术（RP）时的 Gleason 模式可能比活检时更有利。
测试是否可以准确预测降级。
设计、设置和参与者：在监测、流行病学和最终结果数据库（2010-2016 年）中，确定了 6690 名 NCCN 高危 PCa 患者。
我们将整个队列随机分为开发和验证队列（均 n = 3345，50%）。多变量逻辑回归模型使用活检 Gleason、前列腺特异性抗原、阳性前列腺活检核心数和 cT 分期来预测降级。在外部验证队列中，通过准确性、校准和决策曲线分析（DCA）来测试模型。
在 6690 名患者中，50.3%在 RP 时降级，在 2315 名任何活检模式 5 的患者中，44.1%降级为 RP Gleason 模式≤4 + 4。活检 Gleason 模式 5 + 5 的降级率最高（84.1%），而 3 + 4 的降级率最低（4.0%）。在验证队列中，逻辑回归模型衍生的列线图以 71.0%的准确性预测降级，校准中存在轻微偏离（±3.3%）理想预测。在 DCA 中，与治疗所有或不治疗任何策略相比，以及与基于 50.3%平均降级率的算法相比，在所有阈值概率下都记录到净获益。在任何活检 Gleason 模式 5 的亚组中重复了所有步骤，以预测 RP Gleason 模式≤4 + 4。在这里，第二个列线图（n = 2315）产生了 68.0%的准确性，与理想预测的最大偏差为±5.7%，与主要列线图几乎相同的 DCA 模式。
降级发生在一半的高危 PCa 患者中。其存在可以准确预测，并可能有助于更好的治疗计划。
高危前列腺癌患者中有一半会出现降级。我们开发的列线图可以准确预测这些概率。
