Fang Andrew M, Shumaker Luke A, Martin Kimberly D, Jackson Jamaal C, Fan Richard E, Khajir Ghazal, Patel Hiten D, Soodana-Prakash Nachiketh, Vourganti Srinivas, Filson Christopher P, Sonn Geoffrey A, Sprenkle Preston C, Gupta Gopal N, Punnen Sanoj, Rais-Bahrami Soroush
Department of Urology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Cancer. 2022 Sep 15;128(18):3287-3296. doi: 10.1002/cncr.34355. Epub 2022 Jul 12.
Most Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions do not contain clinically significant prostate cancer (CSPCa; grade group ≥2). This study was aimed at identifying clinical and magnetic resonance imaging (MRI)-derived risk fac- tors that predict CSPCa in men with PI-RADS 3 lesions.
This study analyzed the detection of CSPCa in men who underwent MRI-targeted biopsy for PI-RADS 3 lesions. Multivariable logistic regression models with goodness-of-fit testing were used to identify variables associated with CSPCa. Receiver operating curves and decision curve analyses were used to estimate the clinical utility of a predictive model.
Of the 1784 men reviewed, 1537 were included in the training cohort, and 247 were included in the validation cohort. The 309 men with CSPCa (17.3%) were older, had a higher prostate-specific antigen (PSA) density, and had a greater likelihood of an anteriorly located lesion than men without CSPCa (p < .01). Multivariable analysis revealed that PSA density (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.05-1.85; p < .01), age (OR, 1.05; 95% CI, 1.02-1.07; p < .01), and a biopsy-naive status (OR, 1.83; 95% CI, 1.38-2.44) were independently associated with CSPCa. A prior negative biopsy was negatively associated (OR, 0.35; 95% CI, 0.24-0.50; p < .01). The application of the model to the validation cohort resulted in an area under the curve of 0.78. A predicted risk threshold of 12% could have prevented 25% of biopsies while detecting almost 95% of CSPCas with a sensitivity of 94% and a specificity of 34%.
For PI-RADS 3 lesions, an elevated PSA density, older age, and a biopsy-naive status were associated with CSPCa, whereas a prior negative biopsy was negatively associated. A predictive model could prevent PI-RADS 3 biopsies while missing few CSPCas.
Among men with an equivocal lesion (Prostate Imaging-Reporting and Data System 3) on multiparametric magnetic resonance imaging (mpMRI), those who are older, those who have a higher prostate-specific antigen density, and those who have never had a biopsy before are at higher risk for having clinically significant prostate cancer (CSPCa) on subsequent biopsy. However, men with at least one negative biopsy have a lower risk of CSPCa. A new predictive model can greatly reduce the need to biopsy equivocal lesions noted on mpMRI while missing only a few cases of CSPCa.
大多数前列腺影像报告和数据系统(PI-RADS)3类病变不包含具有临床意义的前列腺癌(CSPCa;分级组≥2)。本研究旨在确定预测PI-RADS 3类病变男性患者发生CSPCa的临床和磁共振成像(MRI)衍生风险因素。
本研究分析了因PI-RADS 3类病变接受MRI靶向活检的男性患者中CSPCa的检出情况。采用具有拟合优度检验的多变量逻辑回归模型来识别与CSPCa相关的变量。采用受试者工作特征曲线和决策曲线分析来评估预测模型的临床实用性。
在审查的1784名男性中,1537名纳入训练队列,247名纳入验证队列。与无CSPCa的男性相比,309名患有CSPCa的男性(17.3%)年龄更大,前列腺特异性抗原(PSA)密度更高,且病变位于前部的可能性更大(p < 0.01)。多变量分析显示,PSA密度(比值比[OR],1.36;95%置信区间[CI],1.05 - 1.85;p < 0.01)、年龄(OR,1.05;95% CI,1.02 - 1.07;p < 0.01)和未进行过活检的状态(OR,1.83;95% CI,1.38 - 2.44)与CSPCa独立相关。既往活检阴性与之呈负相关(OR,0.35;95% CI,0.24 - 0.50;p < 0.01)。将该模型应用于验证队列,曲线下面积为0.78。预测风险阈值为12%时,可避免25%的活检,同时检测出近95%的CSPCa,灵敏度为94%,特异性为34%。
对于PI-RADS 3类病变,PSA密度升高、年龄较大和未进行过活检的状态与CSPCa相关,而既往活检阴性与之呈负相关。一种预测模型可避免对PI-RADS 3类病变进行活检,同时漏诊的CSPCa病例很少。
在多参数磁共振成像(mpMRI)上有可疑病变(前列腺影像报告和数据系统3类)的男性中,年龄较大、前列腺特异性抗原密度较高且从未进行过活检的男性,在后续活检中发生具有临床意义的前列腺癌(CSPCa)的风险较高。然而,至少有一次活检阴性的男性发生CSPCa的风险较低。一种新的预测模型可以大大减少对mpMRI上可疑病变进行活检的必要性,同时仅漏诊少数CSPCa病例。