AUTHOR=Liu Junxiao , Yu Shuanbao , Dong Biao , Hong Guodong , Tao Jin , Fan Yafeng , Zhu Zhaowei , Wang Zhiyu , Zhang Xuepei 

TITLE=Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.732027

DOI=10.3389/fonc.2021.732027

ISSN=2234-943X

ABSTRACT=Purpose: The clinical utility of mpMRI for detection and localization of prostate cancer (PCa) has been evaluated and validated. However, the implementation of mpMRI into clinical practice remains some burden of cost and availability for patients and society. We aimed to predict results of prostate mpMRI using clinical parameters and multivariable model to reduce unnecessary multiparametric MRI scans.
Methods: We retrospectively identified 784 men who underwent mpMRI scans and subsequent prostate biopsy between 2016 and 2020 according to inclusion criterion. The cohort was split into a training cohort of 548 (70%) men and a validation cohort of 236 (30%) patients. Clinical parameters including age, PSA derivates and prostate volume were assessed as predictors of mpMRI results. The mpMRI results were divided into groups according to the reports: “negative”, “equivocal”, and “suspicious” for the presence of PCa.
Results: Univariate analysis showed that tPSA, fPSA, PV, and PSAD were significant predictors for suspicious mpMRI (P<0.05). The PSAD (AUC=0.77) and tPSA (AUC=0.74) outperformed fPSA (AUC=0.68) and PV (AUC=0.62) in prediction of mpMRI results. The multivariate model (AUC=0.80) had similar diagnostic accuracy with PSAD (P=0.108), while higher than tPSA (P=0.024) in predicting mpMRI results. The multivariate model illustrated better calibration and substantial improvement in DCA at threshold above 20%. Using PSAD with a 0.13 ng/ml2 cut-off could spare the number of mpMRI scans by 20%, while kept 90% sensitivity in prediction of suspicious MRI-PCa and missed three (3/73, 4%) clinically significant PCa cases. At the same sensitivity level, the multivariate model with a 32% cut-off could spare the number of mpMRI scans by 27%, while missed only one (1/73, 1%) clinically significant PCa case.
Conclusion: Our multivariate model could reduce unnecessary mpMRI scans without comprising the diagnostic ability of clinically significant PCa. Further prospective validation is required.