4.1 Article

The Prostate Health Index and multi-parametric MRI improve diagnostic accuracy of detecting prostate cancer in Asian populations

期刊

INVESTIGATIVE AND CLINICAL UROLOGY
卷 63, 期 6, 页码 631-638

出版社

KOREAN UROLOGICAL ASSOC
DOI: 10.4111/icu.20220056

关键词

Multiparametric magnetic resonance imaging; Prostate cancer; Prostate Health Index

资金

  1. Korea Medical Device Development Fund - Korea government (the Ministry of Science and ICT) [1711138269, KMDF_PR_20200901_0141]
  2. Korea Medical Device Development Fund - Korea government (Ministry of Trade, Industry and Energy) [1711138269, KMDF_PR_20200901_0141]
  3. Korea Medical Device Development Fund - Korea government (Ministry of Health amp
  4. Welfare) [1711138269, KMDF_PR_20200901_0141]
  5. Korea Medical Device Development Fund - Korea government (Ministry of Food and Drug Safety) [1711138269, KMDF_PR_20200901_0141]
  6. National Research Foundation of Korea (NRF) - Korea government (MSIT) [NRF-2020R1F1A1072702]

向作者/读者索取更多资源

This study evaluated the effectiveness of PHI and mpMRI in predicting prostate cancer and clinically significant prostate cancer. The results showed that combining PHI and mpMRI provides the most accurate prediction of both types of prostate cancer. In the absence of mpMRI, PHI is superior to PSA alone as a predictor of prostate cancer, and adding PHI to PSA can increase the detection rate of both prostate cancer and clinically significant prostate cancer.
Purpose: The aim of this study was to evaluate the effectiveness of the Prostate Health Index (PHI) and prostate multi-parametric magnetic resonance imaging (mpMRI) in predicting prostate cancer (PCa) and clinically significant prostate cancer (csPCa) during initial prostate biopsy.Materials and Methods: In total, 343 patients underwent initial prostate biopsy and were screened by use of PHI and prostate -specific antigen (PSA) levels between April 2019 and July 2021. A subgroup of 232 patients also underwent prostate mpMRI. Lo-gistic regression analysis was performed to evaluate the accuracies of PSA, PHI, and mpMRI as predictors of PCa or csPCa. These predictive accuracies were quantified by using the area under the receiver operating characteristic curve. The different predictive models were compared using the DeLong test. Results: Logistic regression showed that age, PSA, PHI, and prostate volume were significant predictors of both PCa and csPCa. In the mpMRI subgroup, age, PSA level, PHI, prostate volume, and mpMRI were predictors of both PCa and csPCa. The PHI (area under the curve [AUC]=0.693) was superior to the PSA level (AUC=0.615) as a predictor of PCa (p=0.038). Combining PHI and mpMRI showed the most accurate prediction of both PCa and csPCa (AUC=0.833, 0.881, respectively).Conclusions:The most accurate prediction of both PCa and csPCa can be performed by combining PHI and mpMRI. In the absence of mpMRI, PHI is superior to PSA alone as a predictor of PCa, and adding PHI to PSA can increase the detection rate of both PCa and csPCa.

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