Department of Urology, Stanford University School of Medicine, Stanford, CA.
Department of Urology, Stanford University School of Medicine, Stanford, CA.
Urol Oncol. 2021 Dec;39(12):831.e19-831.e27. doi: 10.1016/j.urolonc.2021.06.004. Epub 2021 Jul 8.
While multiparametric MRI (mpMRI) has high sensitivity for detection of clinically significant prostate cancer (CSC), false positives and negatives remain common. Calculators that combine mpMRI with clinical variables can improve cancer risk assessment, while providing more accurate predictions for individual patients. We sought to create and externally validate nomograms incorporating Prostate Imaging Reporting and Data System (PIRADS) scores and clinical data to predict the presence of CSC in men of all biopsy backgrounds.
Data from 2125 men undergoing mpMRI and MR fusion biopsy from 2014 to 2018 at Stanford, Yale, and UAB were prospectively collected. Clinical data included age, race, PSA, biopsy status, PIRADS scores, and prostate volume. A nomogram predicting detection of CSC on targeted or systematic biopsy was created.
Biopsy history, Prostate Specific Antigen (PSA) density, PIRADS score of 4 or 5, Caucasian race, and age were significant independent predictors. Our nomogram-the Stanford Prostate Cancer Calculator (SPCC)-combined these factors in a logistic regression to provide stronger predictive accuracy than PSA density or PIRADS alone. Validation of the SPCC using data from Yale and UAB yielded robust AUC values.
The SPCC combines pre-biopsy mpMRI with clinical data to more accurately predict the probability of CSC in men of all biopsy backgrounds. The SPCC demonstrates strong external generalizability with successful validation in two separate institutions. The calculator is available as a free web-based tool that can direct real-time clinical decision-making.
虽然多参数磁共振成像(mpMRI)在检测临床显著前列腺癌(CSC)方面具有较高的敏感性,但假阳性和假阴性仍然很常见。将 mpMRI 与临床变量相结合的计算器可以改善癌症风险评估,同时为个体患者提供更准确的预测。我们旨在创建并验证纳入前列腺影像报告和数据系统(PIRADS)评分和临床数据的列线图,以预测所有活检背景下男性 CSC 的存在。
2014 年至 2018 年,斯坦福大学、耶鲁大学和 UAB 前瞻性地收集了 2125 名接受 mpMRI 和 MR 融合活检的男性的数据。临床数据包括年龄、种族、PSA、活检状态、PIRADS 评分和前列腺体积。创建了一个预测靶向或系统活检中 CSC 检测的列线图。
活检史、前列腺特异性抗原(PSA)密度、PIRADS 评分 4 或 5、白种人种族和年龄是显著的独立预测因素。我们的列线图-斯坦福前列腺癌计算器(SPCC)-将这些因素结合在逻辑回归中,提供了比 PSA 密度或 PIRADS 单独使用更强的预测准确性。使用耶鲁大学和 UAB 的数据对 SPCC 进行验证,得到了稳健的 AUC 值。
SPCC 将术前 mpMRI 与临床数据相结合,更准确地预测了所有活检背景下男性 CSC 的概率。SPCC 在两个独立机构中的成功验证表明具有很强的外部通用性。该计算器是一个免费的基于网络的工具,可以指导实时临床决策。