Ajami Tarek, Han Sunwoo, Porto Joao G, Kimbel Isabella, Szczotka Zoe, Guerard Timothy, VanderVeer-Harris Nathan, Ledesma Braian R, Acosta Patricia Castillo, Kryvenko Oleksandr N, Parekh Dipen J, Stoyanova Radka, Reis Isildinha M, Punnen Sanoj
Desai Sethi Urology Institute, University of Miami, Miller School of Medicine, Miami, FL.
Department of Biostatistics and Bioinformatics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL.
Urol Oncol. 2024 Nov;42(11):370.e9-370.e14. doi: 10.1016/j.urolonc.2024.05.025. Epub 2024 Jul 8.
The recommendation to perform biopsy of PIRADS 3 lesions has not been adopted with strength as compared to higher scored lesions on multiparametric MRI. This represents a challenging scenario and an unmet need for clinicians to apply a risk adapted approach in these cases. In the present study, we examined clinical and radiologic characteristics in men with PI-RADS 3 index lesions that can predict csPCa on mpMRI-target biopsy.
Revision of a prospective database with patients who underwent targeted and systematic biopsies from 2015 to 2023 for PI-RADS 3 lesions identified on mpMRI. Baseline variables were collected, such as PSA density (PSAd), 4Kscore, prostate size, and the apparent diffusion coefficient (ADC) value of the lesion on mpMRI. Logistic regression, receiver operating characteristic (ROC) and decision curve analyses (DCA) assessing the association between clinic-radiologic factors and csPCa were performed.
Overall, 230 patients were included in the study and the median age was 65 years. The median prostate size and PSA were 50 g and 6.26 ng/mL, respectively. 17.4% of patients had csPCa, while 27.5% had Gleason group 1. In univariable logistic analyses, we found that age, BMI, prostate size, PSAd, ADC, and 4Kscore were significant csPCa predictors (P < 0.05). PSAd showed the best prediction performance in terms of AUC (= 0.679). On multivariable analysis, PSAd and 4Kscore were associated with csPCa. The net benefit of PSAd combined with clinical features was superior to those of other parameters. Within patients with PSAd < 0.15, 4Kscore was a statistically significant predictor of csPCa (OR = 3.25, P = 0.032).
PSAd and 4Kscore are better predictors of csPCa in patients with PIRADS 3 lesions compared to ADC. The predictive role of 4Kscore is higher in patients with low PSAd. These results can assist practitioners in the risk stratification of patients with equivocal lesions to determine the need of biopsy.
与多参数磁共振成像（mpMRI）上评分较高的病变相比，对PI-RADS 3类病变进行活检的建议未得到有力采纳。这给临床医生在这些病例中应用风险适应性方法带来了挑战，也存在未满足的需求。在本研究中，我们检查了PI-RADS 3类索引病变男性患者的临床和放射学特征，这些特征可预测mpMRI靶向活检时的临床显著性前列腺癌（csPCa）。
回顾一个前瞻性数据库，该数据库纳入了2015年至2023年因mpMRI上发现的PI-RADS 3类病变接受靶向活检和系统活检的患者。收集基线变量，如前列腺特异抗原密度（PSAd）、4K评分、前列腺大小以及病变在mpMRI上的表观扩散系数（ADC）值。进行逻辑回归、受试者工作特征（ROC）分析和决策曲线分析（DCA），以评估临床放射学因素与csPCa之间的关联。
总体而言，230例患者纳入研究，中位年龄为65岁。前列腺大小中位数和前列腺特异抗原分别为50 g和6.26 ng/mL。17.4%的患者患有csPCa，27.5%的患者为Gleason 1组。在单变量逻辑分析中，我们发现年龄、体重指数、前列腺大小、PSAd、ADC和4K评分是csPCa的显著预测因素（P < 0.05）。就曲线下面积（AUC = 0.679）而言，PSAd显示出最佳预测性能。在多变量分析中，PSAd和4K评分与csPCa相关。PSAd与临床特征相结合的净效益优于其他参数。在PSAd < 0.15的患者中，4K评分是csPCa的统计学显著预测因素（比值比 = 3.25，P = 0.032）。
与ADC相比，PSAd和4K评分是PI-RADS 3类病变患者csPCa的更好预测因素。在PSAd较低的患者中，4K评分的预测作用更高。这些结果可帮助从业者对可疑病变患者进行风险分层，以确定是否需要活检。
