Á Borque-Fernando1, L M Esteban-Escaño2, J Rubio-Briones3, A C Lou-Mercadé4, R García-Ruiz5, A Tejero-Sánchez5, M V Muñoz-Rivero5, T Cabañuz-Plo5, J Alfaro-Torres6, I M Marquina-Ibáñez6, S Hakim-Alonso6, E Mejía-Urbáez6, J Gil-Fabra5, P Gil-Martínez5, R Ávarez-Alegret6, G Sanz7, M J Gil-Sanz5. 1. Servicio de Urología, Hospital Universitario Miguel Servet, Zaragoza, España; Grupo Consolidado de Investigación "Modelos Estocásticos", Gobierno de Aragón, European Social Fund, Zaragoza, España. Electronic address: aborque@salud.aragon.es. 2. Escuela Universitaria Politécnica La Almunia, Zaragoza, España; Grupo Consolidado de Investigación "Modelos Estocásticos", Gobierno de Aragón, European Social Fund, Zaragoza, España. 3. Servicio de Urología, Instituto Valenciano de Oncología, Valencia, España. 4. Hospital Clínico Universitario Lozano Blesa, Zaragoza, España. 5. Servicio de Urología, Hospital Universitario Miguel Servet, Zaragoza, España. 6. Servicio de Anatomía Patológica, Hospital Universitario Miguel Servet, Zaragoza, España. 7. Departamento de Métodos Estadísticos, Facultad de Ciencias, Universidad de Zaragoza, Zaragoza, España; Grupo Consolidado de Investigación "Modelos Estocásticos", Gobierno de Aragón, European Social Fund, Zaragoza, España.
Abstract
INTRODUCTION: To prevent the overdiagnosis and overtreatment of prostate cancer (PC), therapeutic strategies have been established such as active surveillance and focal therapy, as well as methods for clarifying the diagnosis of high-grade prostate cancer (HGPC) (defined as a Gleason score ≥7), such as multiparametric magnetic resonance imaging and new markers such as the 4Kscore test (4KsT). By means of a pilot study, we aim to test the ability of the 4KsT to identify HGPC in prostate biopsies (Bx) and compare the test with other multivariate prognostic models such as the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC 2.0) and the European Research Screening Prostate Cancer Risk Calculator 4 (ERSPC-RC 4). MATERIAL AND METHODS: Fifty-one patients underwent a prostate Bx according to standard clinical practice, with a minimum of 10 cores. The diagnosis of HGPC was agreed upon by 4 uropathologists. We compared the predictions from the various models by using the Mann-Whitney U test, area under the ROC curve (AUC) (DeLong test), probability density function (PDF), box plots and clinical utility curves. RESULTS: Forty-three percent of the patients had PC, and 23.5% had HGPC. The medians of probability for the 4KsT, PCPTRC 2.0 and ERSPC-RC 4 were significantly different between the patients with HGPC and those without HGPC (p≤.022) and were more differentiated in the case of 4KsT (51.5% for HGPC [25-75 percentile: 25-80.5%] vs. 16% [P 25-75: 8-26.5%] for non-HGPC; p=.002). All models presented AUCs above 0.7, with no significant differences between any of them and 4KsT (p≥.20). The PDF and box plots showed good discriminative ability, especially in the ERSPC-RC 4 and 4KsT models. The utility curves showed how a cutoff of 9% for 4KsT identified all cases of HGPC and provided a 22% savings in biopsies, which is similar to what occurs with the ERSPC-RC 4 models and a cutoff of 3%. CONCLUSIONS: The assessed predictive models offer good discriminative ability for HGPCs in Bx. The 4KsT is a good classification model as a whole, followed by ERSPC-RC 4 and PCPTRC 2.0. The clinical utility curves help suggest cutoff points for clinical decisions: 9% for 4KsT and 3% for ERSPC-RC 4. This preliminary study should be interpreted with caution due to its limited sample size.
INTRODUCTION: To prevent the overdiagnosis and overtreatment of prostate cancer (PC), therapeutic strategies have been established such as active surveillance and focal therapy, as well as methods for clarifying the diagnosis of high-grade prostate cancer (HGPC) (defined as a Gleason score ≥7), such as multiparametric magnetic resonance imaging and new markers such as the 4Kscore test (4KsT). By means of a pilot study, we aim to test the ability of the 4KsT to identify HGPC in prostate biopsies (Bx) and compare the test with other multivariate prognostic models such as the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC 2.0) and the European Research Screening Prostate Cancer Risk Calculator 4 (ERSPC-RC 4). MATERIAL AND METHODS: Fifty-one patients underwent a prostate Bx according to standard clinical practice, with a minimum of 10 cores. The diagnosis of HGPC was agreed upon by 4 uropathologists. We compared the predictions from the various models by using the Mann-Whitney U test, area under the ROC curve (AUC) (DeLong test), probability density function (PDF), box plots and clinical utility curves. RESULTS: Forty-three percent of the patients had PC, and 23.5% had HGPC. The medians of probability for the 4KsT, PCPTRC 2.0 and ERSPC-RC 4 were significantly different between the patients with HGPC and those without HGPC (p≤.022) and were more differentiated in the case of 4KsT (51.5% for HGPC [25-75 percentile: 25-80.5%] vs. 16% [P 25-75: 8-26.5%] for non-HGPC; p=.002). All models presented AUCs above 0.7, with no significant differences between any of them and 4KsT (p≥.20). The PDF and box plots showed good discriminative ability, especially in the ERSPC-RC 4 and 4KsT models. The utility curves showed how a cutoff of 9% for 4KsT identified all cases of HGPC and provided a 22% savings in biopsies, which is similar to what occurs with the ERSPC-RC 4 models and a cutoff of 3%. CONCLUSIONS: The assessed predictive models offer good discriminative ability for HGPCs in Bx. The 4KsT is a good classification model as a whole, followed by ERSPC-RC 4 and PCPTRC 2.0. The clinical utility curves help suggest cutoff points for clinical decisions: 9% for 4KsT and 3% for ERSPC-RC 4. This preliminary study should be interpreted with caution due to its limited sample size.
Authors: Miriam Campistol; Juan Morote; Marina Triquell; Lucas Regis; Ana Celma; Inés de Torres; María E Semidey; Richard Mast; Anna Santamaría; Jacques Planas; Enrique Trilla Journal: Cancers (Basel) Date: 2022-05-30 Impact factor: 6.575
Authors: Zachary A Glaser; Jennifer B Gordetsky; Kristin K Porter; Sooryanarayana Varambally; Soroush Rais-Bahrami Journal: Front Oncol Date: 2017-10-30 Impact factor: 6.244