Cancer imaging : the official publication of the International Cancer Imaging Society · 2019
Optimizing prostate cancer accumulating model: combined PI-RADS v2 with prostate specific antigen and its derivative data.
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Lu Yuan-Fei, Zhang Qian, Yao Wei-Gen, Chen Hai-Yan, Chen Jie-Yu, Xu Cong-Cong, Yu Ri-Sheng
Аннотация
BACKGROUND: 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).
METHODS: A 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.
RESULTS: In 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).
CONCLUSIONS: A 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.
METHODS: A 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.
RESULTS: In 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).
CONCLUSIONS: A 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.