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Inicio Actas Urológicas Españolas (English Edition) A preliminary study of the ability of the 4Kscore test, the Prostate Cancer Prev...
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Vol. 40. Issue 3.
Pages 155-163 (April 2016)
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Vol. 40. Issue 3.
Pages 155-163 (April 2016)
Original article
DOI: 10.1016/j.acuroe.2016.02.004
A preliminary study of the ability of the 4Kscore test, the Prostate Cancer Prevention Trial-Risk Calculator and the European Research Screening Prostate-Risk Calculator for predicting high-grade prostate cancer
4Kscore Test, Prostate Cancer Prevention Trial-Risk Calculator y European Research Screening Prostate-Risk Calculator en la predicción del cáncer de próstata de alto grado; estudio preliminar
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Á. Borque-Fernandoa,g,
Corresponding author
aborque@salud.aragon.es

Corresponding author.
, L.M. Esteban-Escañob,g, J. Rubio-Brionesc, A.C. Lou-Mercadéd, R. García-Ruiza, A. Tejero-Sáncheza, M.V. Muñoz-Riveroa, T. Cabañuz-Ploa, J. Alfaro-Torrese, I.M. Marquina-Ibáñeze, S. Hakim-Alonsoe, E. Mejía-Urbáeze, J. Gil-Fabraa, P. Gil-Martíneza, R. Ávarez-Alegrete, G. Sanzf,g, M.J. Gil-Sanza
a Servicio de Urología, Hospital Universitario Miguel Servet, Zaragoza, Spain
b Escuela Universitaria Politécnica La Almunia, Zaragoza, Spain
c Servicio de Urología, Instituto Valenciano de Oncología, Valencia, Spain
d Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
e Servicio de Anatomía Patológica, Hospital Universitario Miguel Servet, Zaragoza, Spain
f Departamento de Métodos Estadísticos, Facultad de Ciencias, Universidad de Zaragoza, Zaragoza, Spain
g Grupo Consolidado de Investigación “Modelos Estocásticos”, Gobierno de Aragón, European Social Fund, Zaragoza, Spain
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Tables (3)
Table 1. Characteristics of the studied patients.
Table 2. Distribution of the probabilities of HGPCa assigned for each predictive model depending on whether it is a HGPCa or not.
Table 3. AUC-ROC of the different models and comparison of the differences between them. The differences between the various AUC of the different calculators are calculated using the DeLong test.
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Additional material (1)
Abstract
Introduction

To prevent the overdiagnosis and overtreatment of prostate cancer (PCa), 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 (HGPCa) (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 HGPCa 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).

Materials and methods

Fifty-one patients underwent a prostate Bx according to standard clinical practice, with a minimum of 10 cores. The diagnosis of HGPCa 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 HGPCa. The medians of probability for the 4KsT, PCPTRC 2.0 and ERSPC-RC 4 were significantly different between the patients with HGPCa and those without HGPCa (p.022) and were more differentiated in the case of 4KsT (51.5% for HGPCa [25–75 percentile: 25–80.5%] vs. 16% [p 25–75: 8–26.5%] for non-HGPCa; 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 HGPCa 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 HGPCas 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.

Keywords:
Prostate cancer
High-grade prostate cancer
4Kscore test
Prostate Cancer Prevention Trial-Risk Calculator
European Research Screening Prostate Cancer-Risk Calculator 4
Clinical utility curves
Predictive models
Prostate biopsy
Validation
Clinical utility
Resumen
Introducción

Frente al sobrediagnóstico y al sobretratamiento en cáncer de próstata (CaP) se establecen estrategias terapéuticas como la vigilancia activa o la terapia focal, o métodos para precisar el diagnóstico del CaP de alto grado (CaP-AG), Gleason7, como la resonancia magnética multiparamétrica o nuevos marcadores como el 4Kscore Test (4KsT).

Es nuestro propósito testar mediante un estudio piloto la capacidad del 4KsT como identificador de CaP-AG (suma de Gleason7) en biopsia de próstata (Bx) y compararlo con otros modelos pronósticos multivariantes disponibles, como el Prostate Cancer Prevention Trial-Risk Calculator 2.0 (PCPTRC 2.0) y el European Research Screening Prostate Cancer-Risk Calculator 4 (ERSPC-RC 4).

Material y métodos

Cincuenta y un pacientes sometidos a BxP según práctica clínica habitual, con un mínimo de 10 cilindros. Diagnóstico de CaP-AG consensuado por 4 uropatólogos. Comparación de las predicciones ofrecidas por los diferentes modelos mediante prueba U Mann-Whitney, áreas bajo la curva ROC (AUC) (test de DeLong), funciones de densidad de probabilidad, diagramas de caja y curvas de utilidad clínica (CUC).

Resultados

Un 43% presentaron CaP y un 23,5% CaP-AG. Las medianas de probabilidad de 4KsT, PCPTRC 2.0 y ERSPC-RC 4 fueron significativamente diferentes entre los pacientes con CaP-AG y no CaP-AG (p0,022), siendo más diferenciadas en el caso de 4KsT (mediana en CaP-AG: 51,5% [percentil 25-75: 25-80,5%], frente a 16% [P 25-75: 8-26,5%] en no CaP-AG [p=0,002]). Todos los modelos mostraron AUC por encima de 0,7 sin diferencias significativas entre ninguno de ellos y 4KsT (p0,20). Las funciones de densidad de probabilidad y diagramas de caja muestran una buena capacidad discriminativa, especialmente en los modelos de ERSPC-RC 4 y 4KsT. Las CUC muestran como un punto de corte del 9% de 4KsT identifica a todos los CaP-AG y permite un ahorro del 22% de biopsias, similar a lo que ocurre con los modelos de ERSPC-RC 4 y un punto de corte del 3%.

Conclusiones

Los modelos predictivos evaluados ofrecen una buena capacidad de discriminación del CaP-AG en Bx. 4KsT es un buen modelo clasificatorio en su conjunto, seguido de ERSPC-RC 4 y PCPTRC 2.0. Las CUC permiten sugerir puntos de corte de decisión clínica: 9% para 4KsT y 3% en ERSPC-RC 4. Este estudio preliminar debe ser interpretado con cautela por su limitado tamaño muestral.

Palabras clave:
Cáncer de próstata
Cáncer de próstata de alto grado
4Kscore Test
Prostate Cancer Prevention Trial-Risk Calculator
European Research Screening Prostate Cancer-Risk Calculator 4
Curvas de utilidad clínica
Modelos predictivos
Biopsia de próstata
Validación
Utilidad clínica

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