Guan Hee Tan1, Gregory Nason1, Khaled Ajib1, Dixon Teck Sing Woon1, Jaime Herrera-Caceres1, Omar Alhunaidi1, Nathan Perlis2. 1. University Health Network-Toronto General Hospital and Princess Margaret Cancer Center, Division of Urology, University of Toronto, 610 University Avenue, 3-130, Toronto, ON, M6G 2M9, Canada. 2. University Health Network-Toronto General Hospital and Princess Margaret Cancer Center, Division of Urology, University of Toronto, 610 University Avenue, 3-130, Toronto, ON, M6G 2M9, Canada. Nathan.perlis@uhn.ca.
Abstract
PURPOSE: Prostate cancer is the second commonest cancer among men. In the large European Randomized Study of Screening for Prostate Cancer (ERSPC) trial, prostate-specific antigen (PSA) screening has been shown to substantially reduce prostate cancer mortality. However, PSA screening is known to lead to more unnecessary prostate biopsies and over-diagnosis of clinically insignificant cancer. Therefore, it is imperative that smarter screening methods be developed to overcome the weaknesses of PSA screening. This review explores the novel screening tools that are available. METHODS: A comprehensive literature search was performed using PubMed regarding newer biomarkers, imaging techniques and risk-predicting models that are used to screen for prostate cancer in mainly biopsy-naïve men. RESULTS: Novel serum-based models like 4Kscore® and prostate health index (PHI) are generally better than PSA alone in detecting clinically significant cancer. Similarly, urine-based biomarkers like prostate cancer antigen 3 (PCA3) and HOXC6/DLX1 have been shown to be more accurate than PSA screening. More recently, multiparametric magnetic resonance imaging (mpMRI) is gaining popularity for its ability to detect clinically significant cancer. There is also evidence that combining individual tests to develop prediction models can reliably predict high-risk prostate cancers while reducing the number of unnecessary biopsies. Combinations such as the Stockholm-3 model (STHLM3) and other novel combinations are presented in this review. CONCLUSION: While we continue to find the smarter screening methods that are reliable, precise, and cost-effective, we continue to advocate shared decision-making in prostate cancer screening in order to work in our patients' best interests.
PURPOSE:Prostate cancer is the second commonest cancer among men. In the large European Randomized Study of Screening for Prostate Cancer (ERSPC) trial, prostate-specific antigen (PSA) screening has been shown to substantially reduce prostate cancermortality. However, PSA screening is known to lead to more unnecessary prostate biopsies and over-diagnosis of clinically insignificant cancer. Therefore, it is imperative that smarter screening methods be developed to overcome the weaknesses of PSA screening. This review explores the novel screening tools that are available. METHODS: A comprehensive literature search was performed using PubMed regarding newer biomarkers, imaging techniques and risk-predicting models that are used to screen for prostate cancer in mainly biopsy-naïve men. RESULTS: Novel serum-based models like 4Kscore® and prostate health index (PHI) are generally better than PSA alone in detecting clinically significant cancer. Similarly, urine-based biomarkers like prostate cancer antigen 3 (PCA3) and HOXC6/DLX1 have been shown to be more accurate than PSA screening. More recently, multiparametric magnetic resonance imaging (mpMRI) is gaining popularity for its ability to detect clinically significant cancer. There is also evidence that combining individual tests to develop prediction models can reliably predict high-risk prostate cancers while reducing the number of unnecessary biopsies. Combinations such as the Stockholm-3 model (STHLM3) and other novel combinations are presented in this review. CONCLUSION: While we continue to find the smarter screening methods that are reliable, precise, and cost-effective, we continue to advocate shared decision-making in prostate cancer screening in order to work in our patients' best interests.
Authors: Olga Katzendorn; Christoph A J von Klot; Samy Mahjoub; Pouriya Faraj Tabrizi; Nina N Harke; Hossein Tezval; Susanne Hellms; Jörg Hennenlotter; Mirza S Baig; Arnulf Stenzl; Ferdinand Seith; Marcel Lafos; Markus A Kuczyk; Steffen Rausch; Inga Peters Journal: PLoS One Date: 2022-08-12 Impact factor: 3.752
Authors: Diego Fernández-Lázaro; Juan Luis García Hernández; Alberto Caballero García; Alfredo Córdova Martínez; Juan Mielgo-Ayuso; Juan Jesús Cruz-Hernández Journal: Diagnostics (Basel) Date: 2020-04-13