Gaudiano Caterina, Braccischi Lorenzo, Taninokuchi Tomassoni Makoto, Paccapelo Alexandro, Bianchi Lorenzo, Corcioni Beniamino, Ciccarese Federica, Schiavina Riccardo, Droghetti Matteo, Giunchi Francesca, Fiorentino Michelangelo, Brunocilla Eugenio, Golfieri Rita
Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Front Oncol. 2023 Feb 20;13:1082564. doi: 10.3389/fonc.2023.1082564. eCollection 2023.
To evaluate multiparametric magnetic resonance imaging (mpMRI) parameters, such as TransPA (transverse prostate maximum sectional area), TransCGA (transverse central gland sectional area), TransPZA (transverse peripheral zone sectional area), and TransPAI (TransPZA/TransCGA ratio) in predicting prostate cancer (PCa) in prostate imaging reporting and data system (PI-RADS) 3 lesions.
Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), the area under the receiver operating characteristic curve (AUC), and the best cut-off, were calculated. Univariate and multivariate analyses were carried out to evaluate the capability to predict PCa.
Out of 120 PI-RADS 3 lesions, 54 (45.0%) were PCa with 34 (28.3%) csPCas. Median TransPA, TransCGA, TransPZA and TransPAI were 15.4cm, 9.1cm, 5.5cm and 0.57, respectively. At multivariate analysis, location in the transition zone (OR=7.92, 95% CI: 2.70-23.29, P<0.001) and TransPA (OR=0.83, 95% CI: 0.76-0.92, P<0.001) were independent predictors of PCa. The TransPA (OR=0.90, 95% CI: 0.082-0.99, P=0.022) was an independent predictor of csPCa. The best cut-off of TransPA for csPCa was 18 (Sensitivity 88.2%, Specificity 37.2%, PPV 35.7%, NPV 88.9%). The discrimination (AUC) of the multivariate model was 0.627 (95% CI: 0.519-0.734, P<0.031).
In PI-RADS 3 lesions, the TransPA could be useful in selecting patients requiring biopsy.
评估多参数磁共振成像（mpMRI）参数，如经前列腺横截面积（TransPA）、经中央腺体横截面积（TransCGA）、经外周带横截面积（TransPZA）和经外周带与中央腺体面积比（TransPAI）在前列腺影像报告和数据系统（PI-RADS）3类病变中预测前列腺癌（PCa）的价值。
计算敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）、受试者操作特征曲线下面积（AUC）以及最佳截断值。进行单因素和多因素分析以评估预测PCa的能力。
120例PI-RADS 3类病变中，54例（45.0%）为PCa，其中34例（28.3%）为临床有意义的前列腺癌（csPCa）。TransPA、TransCGA、TransPZA和TransPAI的中位数分别为15.4cm²、9.1cm²、5.5cm²和0.57。多因素分析显示，移行带位置（OR=7.92，95%CI：2.70-23.29，P<0.001）和TransPA（OR=0.83，95%CI：0.76-0.92，P<0.001）是PCa的独立预测因素。TransPA（OR=0.90，95%CI：0.082-0.99，P=0.022）是csPCa的独立预测因素。csPCa的TransPA最佳截断值为18（敏感性88.2%，特异性37.2%，PPV 35.7%，NPV 88.9%）。多因素模型的鉴别能力（AUC）为0.627（95%CI：0.519-0.734，P<0.031）。
在PI-RADS 3类病变中，TransPA有助于选择需要活检的患者。
