CC BY-NC-ND 4.0 · Revista Urología Colombiana / Colombian Urology Journal 2020; 29(03): 129-135
DOI: 10.1055/s-0040-1713378
Original Article | Artículo Original
Urologic Oncology/Urología Oncológica

Predicting the Probability of Lymph Node Involvement with Prostate Cancer Nomograms: Can We Trust the Prediction Models?

Predicción de la probabilidad de compromiso ganglionar con nomogramas para cáncer de próstata. ¿Podemos confiar en los modelos de predicción?
1   Division of Urology, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
,
Catalina Barco-Castillo
2   Department of Urology, Hospital Universitario, Fundación Santa Fe de Bogotá, Bogotá, Colombia
,
Jessica Santander
2   Department of Urology, Hospital Universitario, Fundación Santa Fe de Bogotá, Bogotá, Colombia
,
Laura Zuluaga
2   Department of Urology, Hospital Universitario, Fundación Santa Fe de Bogotá, Bogotá, Colombia
,
Camilo Medina
2   Department of Urology, Hospital Universitario, Fundación Santa Fe de Bogotá, Bogotá, Colombia
,
Carlos Trujillo
2   Department of Urology, Hospital Universitario, Fundación Santa Fe de Bogotá, Bogotá, Colombia
,
Mauricio Plata
2   Department of Urology, Hospital Universitario, Fundación Santa Fe de Bogotá, Bogotá, Colombia
,
Juan Ignacio Caicedo
2   Department of Urology, Hospital Universitario, Fundación Santa Fe de Bogotá, Bogotá, Colombia
› Author Affiliations

Abstract

Introduction Prediction of lymph node involvement (LNI) is of paramount importance for patients with prostate cancer (PCa) undergoing radical prostatectomy (RP). Multiple statistical models predicting LNI have been developed to support clinical decision-making regarding the need of extended pelvic lymph node dissection (ePLND). Our aim is to evaluate the prediction ability of the best-performing prediction tools for LNI in PCa in a Latin-American population.

Methods Clinicopathological data of 830 patients with PCa who underwent RP and ePLND between 2007 and 2018 was obtained. Only data from patients who had ≥ 10 lymph nodes (LNs) harvested were included (n = 576 patients). Four prediction models were validated using this cohort: The Memorial Sloan Kettering Cancer Center (MSKCC) web calculator, Briganti v.2017, Yale formula and Partin tables v.2016. The performance of the prediction tools was assessed using the area under the receiver operating characteristic (ROC) curve (AUC).

Results The median age was 61 years old (interquartile range [IQR] 56–66), the median Prostate specific antigen (PSA) was 6,81 ng/mL (IQR 4,8–10,1) and the median of LNs harvested was 17 (IQR 13–23), and LNI was identified in 53 patients (9.3%). Predictions from the 2017 Briganti nomogram AUC (0.85) and the Yale formula AUC (0.85) were the most accurate; MSKCC and 2016 Partin tables AUC were both 0,84.

Conclusion There was no significant difference in the performance of the four validated prediction tools in a Latin-American population compared with the European or North American patients in whom these tools have been validated. Among the 4 models, the Briganti v.2017 and Yale formula yielded the best results, but the AUC overlapped with the other validated models.

Resumen

Introducción La predicción del compromiso ganglionar es de suma importancia en pacientes con cáncer de próstata (CaP) que se van a someter a prostatectomía radical (PR). Múltiples modelos estadísticos se han desarrollado para predecir el riesgo de compromiso ganglionar y facilitar las decisiones clínicas de realizar o no linfadenectomía pélvica ampliada (LPA). Nuestro objetivo es evaluar la habilidad de predicción de las mejores herramientas de predicción de compromiso ganglionar en CaP en una población latinoamericana.

Métodos Se evaluaron los datos clínico-patológicos de 830 pacientes con CaP sometidos a PR y LPA entre el 2007–2018. Solo se analizaron os pacientes con 10 o más ganglios extraídos (n = 576). Cuatro modelos de predicción fueron validados en esta cohorte: el modelo de la calculadora online del Memorial Sloan Kettering Cancer Center (MSKCC), el Briganti v.2017, la fórmula de Yale, y tablas de Partin v.2016. Se evaluó el desempeño de los modelos con curvas de características operativas del receptor (COR) y el área bajo la curva (ABC).

Resultados La mediana de edad fue 61 años (rango intercuartílico [RI]: 56–66), mediana de Prostate specific antigen (PSA) 6,81 ng/mL (RI: 4,8–10,1), y mediana de ganglios extraídos 17 (RI: 13–23); se documentó compromiso ganglionar en 53 pacientes (9.3%). La habilidad de predicción del nomograma de Briganti v.2017 ABC (0,85) y la fórmula de Yale ABC (0,85) fueron las más precisas. El modelo del MSKCC y las tablas de Partin v.2016 mostraron AUC de 0,84 ambos.

Conclusiones No encontramos diferencia estadisticamente significativa en el desempeño de los cuatro modelos de predicción validados en esta población latinoamericana comparada con pacientes norteamericanos o europeos en los que estas herramientas fueron desarrolladas. Entre los 4 modelos, el nomograma de Briganti v.2017 y la fórmula de Yale mostraron los mejores resultados; sin embargo, el AUC se sobrepone con los otros modelos validados.



Publication History

Received: 27 January 2020

Accepted: 15 April 2020

Article published online:
04 August 2020

© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Sociedad Colombiana de Urología. Publicado por Thieme Revinter Publicações Ltda
Rio de Janeiro, Brazil

 
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