AUTHOR=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 

TITLE=Transverse prostate maximum sectional area can predict clinically significant prostate cancer in PI-RADS 3 lesions at multiparametric magnetic resonance imaging

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1082564

DOI=10.3389/fonc.2023.1082564

ISSN=2234-943X

ABSTRACT=Background: 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.
Methods: 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.
Results: Out of 120 PI-RADS 3 lesions, 54 (45.0%) were PCa with 34 (63.0%) csPCas. Median TransPA, TransCGA, TransPZA and TransPAI were 15.4cm2, 9.1cm2, 5.5cm2 and 0.57, respectively. At multivariate analysis, lesion location (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 best cut-off of TransPA for PCa was 18cm2 (Sensitivity 88.9%, Specificity 45.5%, PPV 83.3%, NPV 57.1%). The AUC of the multivariate model was 0.779 (95% CI: 0.697-0.860, P<0.001). The TransPA (OR=0.90, 95% CI: 0.082-0.99, P=0.022) was an independent predictor of csPCa.
Conclusions: In PI-RADS 3 lesions, the TransPA could be useful in selecting patients requiring biopsy.