ArticleJournal of cancer research and clinical oncology2017
Prostate transitional zone volume-based nomogram for predicting prostate cancer and high progression prostate cancer in a real-world population.
Article in Journal of cancer research and clinical oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A nomogram using transition zone PSA density for detecting clinically significant prostate cancer in PI-RADS 3 lesions.Scientific reports · 2025Article
- Gland- and cell-level heterogeneity in the prostate: A narrative review of related diseases.Current urology · 2025Article
- Transition zone-based prostate-specific antigen density for differentiating clinically significant prostate cancer in PI-RADS score 3 lesions.Scientific reports · 2025Article
- Prostate zones and cancer: lost in transition?Nature reviews. Urology · 2022Review
- MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer.Frontiers in oncology · 2021Article
- Zonal adjusted PSA density improves prostate cancer detection rates compared with PSA in Taiwanese males with PSA < 20 ng/ml.BMC urology · 2020Article
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Authors and funding
13 authors.
Funding
Abstract
purposeTo evaluate and compare the efficacy of prostate volume (PV), transitional zone volume (TZV), and prostate volume index (PVI, the ratio of TZV to peripheral zone volume) in the identification of men at risk of prostate cancer (PCa) and high-progression PCa (HPPCa) at the initial biopsy (IBX) in a real-world population.
methodsFrom Jul 2014 to Aug 2016, data on 1144 patients who had undergone the initial prostate biopsies were prospectively collected and analyzed. Univariate and multivariate logistic regression analyses were performed to identify the independent predictors for PCa and HPPCa. Based on independent predictors, nomogram models were developed and internally validated to assess a man's risk of harboring PCa and HPPCa.
resultsThe detection rates of PCa and HPPCa were 43.09% (493/1144) and 39.16% (448/1144), respectively. In the multivariate analyses, age, PSA, TZV, DRE, and TRUS instead of PV or PVI were independent predictors for PCa and HPPCa, percent free PSA was independent predictor for PCa not for HPPCa. Such independent predictors were finally included in the nomogram models. The AUCs of TZV-based nomogram models were 87.0% for PCa and 87.7% for HPPCa, which were higher than that of PSA alone or other predictive models.
conclusionsTZV is a better predictive biomarker than PV or PVI for PCa and HPPCa, we recommend adding TZV but not PV or PVI to the nomogram models to improve the predictive accuracy of PCa and HPPCa at IBX.
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