Journal of the Formosan Medical Association = Taiwan yi zhi · 2025
Prediction of clinically significant prostate cancer using extra-transitional zone prostate-specific antigen among Taiwanese men.
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Yu Jung-Yang, Liu Shin-Ping, Huang Chao-Yuan, Jeff Chueh Shih-Chieh, Chen Chung-Hsin, Pu Yeong-Shiau
Аннотация
PURPOSE: To validate the use of estimated post-enucleation prostate-specific antigen (EPEPSA) and extra-transitional zone density (EtzD) in predicting clinically significant prostate cancer (csPC) in a Taiwanese cohort.
MATERIALS AND METHODS: Between August 2017 and June 2022, patients with PSA levels <20 ng/mL who underwent prostate biopsy at the National Taiwan University Hospital were enrolled. According to a model built by a previous cohort, EPEPSA was calculated using the following formula: 1.068 + 0.016 × PSA +0.004 × prostate volume - 1.02 × adenoma volume/prostate volume. The EtzD was calculated by dividing the EPEPSA value by the peripheral zone volume, and csPC was defined as favorable-intermediate, unfavorable-intermediate, high, very-high-risk or metastatic prostate cancer, according to the National Comprehensive Cancer Network guidelines.
RESULTS: A total of 229 (32.1%) patients with csPC were diagnosed in the enrolled cohort (N = 714). Both EPEPSA and EtzD were statistically associated with csPC prediction (odds ratio [OR]: 29.56, 95% confidence interval [CI]: 5.41-161.6; OR: 4.11, 95% CI: 2.20-7.67, respectively). However, PSA density (PSAD) (area under the curve [AUC]: 0.77) still had the best predictive value compared with PSA, EPEPSA, and EtzD (AUC: 0.68, 0.64, and 0.67, respectively; all p < 0.05). There were no significant differences in the csPC prediction values of EPEPSA, EtzD, and PSA.
CONCLUSION: Although EPEPSA and EtzD can predict csPC in Taiwan, they failed to outperform PSAD and PSA. Therefore, PSAD is the best predictor for csPC.
MATERIALS AND METHODS: Between August 2017 and June 2022, patients with PSA levels <20 ng/mL who underwent prostate biopsy at the National Taiwan University Hospital were enrolled. According to a model built by a previous cohort, EPEPSA was calculated using the following formula: 1.068 + 0.016 × PSA +0.004 × prostate volume - 1.02 × adenoma volume/prostate volume. The EtzD was calculated by dividing the EPEPSA value by the peripheral zone volume, and csPC was defined as favorable-intermediate, unfavorable-intermediate, high, very-high-risk or metastatic prostate cancer, according to the National Comprehensive Cancer Network guidelines.
RESULTS: A total of 229 (32.1%) patients with csPC were diagnosed in the enrolled cohort (N = 714). Both EPEPSA and EtzD were statistically associated with csPC prediction (odds ratio [OR]: 29.56, 95% confidence interval [CI]: 5.41-161.6; OR: 4.11, 95% CI: 2.20-7.67, respectively). However, PSA density (PSAD) (area under the curve [AUC]: 0.77) still had the best predictive value compared with PSA, EPEPSA, and EtzD (AUC: 0.68, 0.64, and 0.67, respectively; all p < 0.05). There were no significant differences in the csPC prediction values of EPEPSA, EtzD, and PSA.
CONCLUSION: Although EPEPSA and EtzD can predict csPC in Taiwan, they failed to outperform PSAD and PSA. Therefore, PSAD is the best predictor for csPC.