Bjurlin Marc A, Rosenkrantz Andrew B, Sarkar Saradwata, Lepor Herbert, Huang William C, Huang Richard, Venkataraman Rajesh, Taneja Samir S
Division of Urologic Oncology, Department of Urology, New York University Langone Medical Center, New York, NY.
Department of Radiology, New York University Langone Medical Center, New York, NY.
Urology. 2018 Feb;112:112-120. doi: 10.1016/j.urology.2017.09.035. Epub 2017 Nov 16.
To develop nomograms that predict the probability of overall prostate cancer (PCa) and clinically significant PCa (Gleason ≥7) on magnetic resonance imaging (MRI)-targeted, and combined MRI-targeted and systematic, prostate biopsy.
From June 2012 to August 2014, magnetic resonance imaging to ultrasound fusion-targeted prostate biopsy was performed on 464 men with suspicious regions identified on pre-biopsy 3T MRI along with systematic 12 core biopsy. Logistic regression modeling was used to evaluate predictors of overall and clinically significant PCa, and corresponding nomograms were generated for men who were not previously biopsied or had 1 or more prior negative biopsies. Models were created with 70% of a randomly selected training sample and bias-corrected using bootstrap resampling. The models were then validated with the remaining 30% testing sample pool.
A total of 459 patients were included for analysis (median age 66 years, prostate-specific antigen [PSA] 5.2 ng/mL, prostate volume 49 cc). Independent predictors of PCa on targeted and systematic prostate biopsy were PSA density, age, and MRI suspicion score. PCa probability nomograms were generated for each cohort using the predictors. Bias-corrected areas under the receiver-operating characteristic curves for overall and clinically significant PCa detection were 0.82 (0.78) and 0.91 (0.84) for men without prior biopsy and 0.76 (0.65) and 0.86 (0.87) for men with a prior negative biopsy in the training (testing) samples.
PSA density, age, and MRI suspicion score predict PCa on combined MRI-targeted and systematic biopsy. Our generated nomograms demonstrate high diagnostic accuracy and may further aid in the decision to perform biopsy in men with clinical suspicion of PCa.
开发列线图,以预测在磁共振成像(MRI)靶向及MRI靶向联合系统前列腺穿刺活检中,前列腺癌(PCa)总体发病概率及具有临床意义的PCa( Gleason评分≥7)发病概率。
2012年6月至2014年8月,对464例在活检前3T MRI上发现可疑区域的男性患者进行了磁共振成像引导下超声融合靶向前列腺穿刺活检以及系统的12针穿刺活检。采用逻辑回归模型评估PCa总体发病及具有临床意义的PCa的预测因素,并为之前未进行过活检或有1次或更多次既往阴性活检的男性患者生成相应的列线图。模型使用随机选择的70%训练样本创建,并通过自助重采样进行偏差校正。然后使用其余30%的测试样本池对模型进行验证。
共纳入459例患者进行分析(中位年龄66岁,前列腺特异性抗原[PSA] 5.2 ng/mL,前列腺体积49 cc)。靶向及系统前列腺穿刺活检中PCa的独立预测因素为PSA密度、年龄和MRI可疑评分。使用这些预测因素为每个队列生成PCa概率列线图。在训练(测试)样本中,对于未进行过活检的男性,总体及具有临床意义的PCa检测的接受者操作特征曲线下偏差校正面积分别为0.82(0.78)和0.91(0.84),对于有既往阴性活检的男性分别为0.76(0.65)和0.86(0.87)。
PSA密度、年龄和MRI可疑评分可预测MRI靶向联合系统活检中的PCa。我们生成的列线图显示出较高的诊断准确性,可能有助于进一步辅助临床怀疑PCa的男性患者进行活检决策。