Optimizing prostate cancer accumulating model: combined PI-RADS v2 with prostate specific antigen and its derivative data

Yuan-Fei Lu,Wei-Gen Yao,Jie-Yu Chen,Cong-Cong Xu,Ri-Sheng Yu,Hai-Yan Chen,Qian Zhang

doi:10.1186/s40644-019-0208-6

Yuan-Fei Lu, Wei-Gen Yao + Show 5 more

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https://doi.org/10.1186/s40644-019-0208-6

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Abstract

BackgroundTo establish a new accumulating model to enhance the accuracy of prostate cancer (PCa) diagnosis by incorporating prostate-specific antigen (PSA) and its derivative data into the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2).MethodsA total of 357 patients who underwent prostate biopsy between January 2014 and December 2017 were included in this study. All patients had 3.0 T multiparametric magnetic resonance imaging (MRI) and complete laboratory examinations. PI-RADS v2 was used to assess the imaging. PSA, PSA density (PSAD), the free/total PSA ratio (f/t PSA) and the Gleason score (GS) were classified into four-tiered levels, and optimal weights were pursued on these managed levels to build a PCa accumulating model. A receiver operating characteristic curve was generated.ResultsIn all, 174 patients (48.7%) had benign prostatic hyperplasia, and 183 (51.3%) had PCa, among whom 149 (81.4%, 149/183) had clinically significant PCa. The established model 6 (PI-RADS v2 + level of PSAD + level of f/t PSA+ level of PSA) had a sensitivity and specificity of 81.4 and 84.5%, respectively, at the cut-off point of 11 in PCa diagnosis. Correspondingly, at the 12 cut-off point, the sensitivity and specificity were 87.7 and 83.0%, respectively, in diagnosing clinically significant PCa. The score of the new accumulating system was significantly different among the defined GS groups (p < 0.001). The mean values and 95% confidence intervals for GS 1–4 groups were 10.20 (9.63–10.40), 12.03 (11.19–12.87), 14.12 (13.60–14.64) and 15.44 (15.09–15.79).ConclusionsA new PCa accumulating model may be useful in improving the accuracy of the primary diagnosis of PCa and helpful in the clinical decision to perform a biopsy when MRI results are negative.

Highlights

To establish a new accumulating model to enhance the accuracy of prostate cancer (PCa) diagnosis by incorporating prostate-specific antigen (PSA) and its derivative data into the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2)
The variable levels of PSA, PSA density (PSAD), free/total PSA ratio (f/t PSA) and PI-RASD v2 were clearly related to the presence of PCa and Clinically significant PCa (CS PCa), especially PI-RADS v2 and the level of PSAD
The levels of PSA and f/t PSA were rejected in the PCa diagnosis in the logistic regression (Table 2)

Summary

Introduction

To establish a new accumulating model to enhance the accuracy of prostate cancer (PCa) diagnosis by incorporating prostate-specific antigen (PSA) and its derivative data into the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2). In 2014, the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) was published to simplify and standardize the terminology and content of radiology reports [3, 4]. Prostate-specific antigen (PSA) screening is widely used to assess PCa, despite its false-positive rate and its overtreatment because of its inferior accuracy. The cut-off point of 4 ng/mL is not sufficient to evaluate the risk of PCa [6]. It is still the first index used to detect the presence of PCa [7]. PSA density (PSAD) and the free/total PSA ratio (f/t PSA), clinical statistics available from PSA, are considered more sensitive in predicting PCa [8, 9]

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