The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer

Autoři: Song Zheng aff001;  Shaoqin Jiang aff001;  Zhenlin Chen aff001;  Zhangcheng Huang aff001;  Wenzhen Shi aff001;  Bingqiao Liu aff001;  Yue Xu aff001;  Yinan Guo aff002;  Huijie Yang aff001;  Mengqiang Li aff001
Působiště autorů: Laboratory of Urology, Department of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, China aff001;  Department of Nursing, Laboratory of Urology, Department of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, China aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0218645


Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

Klíčová slova:

Biopsy – Cancer detection and diagnosis – Magnetic resonance imaging – Prostate cancer – Prostate gland – Regression analysis


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2019 Číslo 11