Open AccessProstate volume does not provide additional predictive value to prostate health index for prostate cancer or clinically significant prostate cancer: results from a multicenter study in China
Author(s) -
Da Huang,
Yishuo Wu,
Dingwei Ye,
Jun Qi,
Fang Liu,
Brian T. Helfand,
S. Lilly Zheng,
Qiang Ding,
Danfeng Xu,
Rong Na,
Jianfeng Xu,
Yinghao Sun
Publication year - 2020
Publication title -
asian journal of andrology/asian journal of andrology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.701
H-Index - 74
eISSN - 1745-7262
pISSN - 1008-682X
DOI - 10.4103/aja.aja_136_19
Subject(s) - medicine , prostate cancer , logistic regression , prostate , cohort , prostate specific antigen , receiver operating characteristic , cohort study , prospective cohort study , oncology , cancer , urology
To evaluate whether prostate volume (PV) would provide additional predictive utility to the prostate health index (phi) for predicting prostate cancer (PCa) or clinically significant prostate cancer, we designed a prospective, observational multicenter study in two prostate biopsy cohorts. Cohort 1 included 595 patients from three medical centers from 2012 to 2013, and Cohort 2 included 1025 patients from four medical centers from 2013 to 2014. Area under the receiver operating characteristic curves (AUC) and logistic regression models were used to evaluate the predictive performance of PV-based derivatives and models. Linear regression analysis showed that both total prostate-specific antigen (tPSA) and free PSA (fPSA) were significantly correlated with PV (all P < 0.05). [-2]proPSA (p2PSA) was significantly correlated with PV in Cohort 2 (P< 0.001) but not in Cohort 1 (P= 0.309), while no significant association was observed between phi and PV. When combining phi with PV, phi density (PHID) and another phi derivative (PHIV, calculated as phi/PV 0.5 ) did not outperform phi for predicting PCa or clinically significant PCa in either Cohort 1 or Cohort 2. Logistic regression analysis also showed that phi and PV were independent predictors for both PCa and clinically significant PCa (all P < 0.05); however, PV did not provide additional predictive value to phi when combining these derivatives in a regression model (all models vs phi were not statistically significant, all P > 0.05). In conclusion, PV-based derivatives (both PHIV and PHID) and models incorporating PV did not improve the predictive abilities of phi for either PCa or clinically significant PCa.