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

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] 56­66), the median Prostate specific antigen (PSA) was 6,81 ng/mL (IQR 4,8­10,1) and the median of LNs harvested was 17 (IQR 13­23), 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.

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 2007­2018. 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]: 56­66), mediana de Prostate specific antigen (PSA) 6,81 ng/mL (RI: 4,8­10,1), y mediana de ganglios extraídos 17 (RI: 13­23); 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.

Value of Gailina nomogram in predicting seminal vesicle invasion in prostate cancer / 中华泌尿外科杂志

Objective To evaluate the accuracy of Gallina nomogram in predicting seminal vesicle invasion (SVI) in prostate cancer.Methods From January 2009 to December 2011,89 patients with prostate carcinoma underwent open retropubic or laparoscopic radical prostatectomy.Complete data of preoperative serum prostate specific antigen (PSA),clinical stage,biopsy Gleason score,percentage of positive biopsy cores,pelvic MRI and pathological report of prostatectomy specimen were collected,and all the patients met the inclusion criteria of Gallina nomogram,2001 Partin tables and 2007 Partin tables.Postoperative pathological results were respectively compared with MRI and the incidence of SVI predicted by the three tools,and the sensitivity,specificity and accuracy of MRI in predicting SVI were calculated.The receiver operating characteristics curves were performed to test the predictive accuracy of SVI of each tool.Results The incidences of organ-confined disease,capsule invasion,SVI and lymph node metastasis were 58.4％,16.9％,22.5％,and 2.2％,respectively.The sensitivity,specificity and accuracy of MRI in predicting SVI was 45.0％ (9/20),71.0％ (49/69) and 65.2％ (58/89),respectively.The area under the curve (AUC) for SVI disease prediction of 2001 Partin tables,2007 Partin tables and Gallina nomogram was 0.712,0.711 and 0.801,respectively.Conclusions The sensitivity of MRI in predicting SVI is poor,the specificity and accuracy are common.All the predictive tools have a reasonable value for SVI (AUC ＞ 0.7),and Gallina nomogram is superior to two versions of Partin tables in predicting SVI.

Predictive accuracy of different Partin tables in Chinese prostate cancer patients / 中华泌尿外科杂志

Objective To validate the Partin table 1997,2001 and 2007 for their accuracy in predicting pathologic stage in Chinese prostate cancer patients.Methods From January 1997 to June 2007,109 consecutive patients with clinically localized prostate carcinoma underwent open retropubic or laparoscopic radical prostatectomies and met all inclusion criteria well enrolled.Receiver operating characteristic(ROC)analysis was performed tO test the predictive accuracy of organ confined disease (0CD),extraprostatic extension(EPE),seminal vesicle involvement(SVI)and lymph node involvement(LNI). Results OCD,EPE,SVl and LNl were noted in 70%,17%,13%and 0%of cases respectively.The area under curve(AUC)of ROC for Partin table 1997 was 0.727,0.654 and 0.811for 0CD.EPE and SVl respectively,and was 0.693,0.633 and 0.835 for Partin table 2001 and 0.669.0.611 and 0.778 for Partin table 2007.Conclusions Partin tables 1997,2001 and 2007 are able to accurately predict the pathologic feature of seminal vesicle involvement.However,only Partin table 1997 can more accurately predict organ confined disease in this external validation for Chinese patients.