AUTHOR=Pan Jin-feng , Su Rui , Cao Jian-zhou , Zhao Zhen-ya , Ren Da-wei , Ye Sha-zhou , Huang Rui-da , Tao Zhu-lei , Yu Cheng-ling , Jiang Jun-hui , Ma Qi 

TITLE=Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.740868

DOI=10.3389/fonc.2021.740868

ISSN=2234-943X

ABSTRACT=Purpose: The purpose of this study is to explore the value of combining bpMRI and clinical indicators in diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making. 
Methods: We retrospectively analyzed 530 patients underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n=371,70%) and validation group (n=159,30%). All patients underwent prostate bpMRI examination and collected T2-weighted imaging (T2WI) and Diffusion-weighted imaging (DWI) sequences before biopsy, and were scored respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then performed a PI-RADS scoring. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analysing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.
Results: In the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed the PI-RADS score, Total score, f/tPSA and PSA density (PSAD) were independent predictors of csPCa. The prediction model which defined by Total score, f/tPSA and PSAD had the highest discriminatory power of csPCa (AUC=0.931), the diagnostic sensitivity and specificity were 85.1% and 87.5% respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit both in the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators. 
Conclusion: The prediction model and Nomogram based on bpMRI and clinical indicators exhibits a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.