INTRODUCTION: The PRECE is a model predicting the risk of extracapsular extension (ECE) of prostate cancer: it has been developed on more than 6000 patients who underwent robotic radical prostatectomy (RARP) at the Global Robotic Institute, FL, USA. Up to now, it is the single tool predicting either the side and the amount of ECE. The model has a free user-friendly interface and is made up from simple and available covariates, namely age, PSA, cT, GS and percent of positive core, the latter topographically distributed within the prostate gland. Despite the successful performance at internal validation, the model is still lacking an external validation (EV). The aim of the paper is to externally validate the PRECE model on an Italian cohort of patients elected to RARP. METHODS: 269 prostatic lobes from 141 patients represented the validation dataset. The EV was performed with the receiver operating characteristics (ROC) curves and calibration, to address the ability of PRECE to discriminate between patients with or without ECE. RESULTS: Overall, an ECE was found in 91 out of the 269 prostatic lobes (34%). Twenty-five patients out of pT3 had a bilateral ECE. The ROC curve showed an AUC of 0.80 (95% CI 0.74-0.85). Sensitivity and specificity were 77% and 69%, respectively. The model showed an acceptable calibration with tendency towards overestimation. CONCLUSIONS: From the current EV, the PRECE displays a good predictive performance to discriminate between cases with and without ECE; despite preliminary, outcomes may support the generalizability of the model in dataset other than the development one.
INTRODUCTION: The PRECE is a model predicting the risk of extracapsular extension (ECE) of prostate cancer: it has been developed on more than 6000 patients who underwent robotic radical prostatectomy (RARP) at the Global Robotic Institute, FL, USA. Up to now, it is the single tool predicting either the side and the amount of ECE. The model has a free user-friendly interface and is made up from simple and available covariates, namely age, PSA, cT, GS and percent of positive core, the latter topographically distributed within the prostate gland. Despite the successful performance at internal validation, the model is still lacking an external validation (EV). The aim of the paper is to externally validate the PRECE model on an Italian cohort of patients elected to RARP. METHODS: 269 prostatic lobes from 141 patients represented the validation dataset. The EV was performed with the receiver operating characteristics (ROC) curves and calibration, to address the ability of PRECE to discriminate between patients with or without ECE. RESULTS: Overall, an ECE was found in 91 out of the 269 prostatic lobes (34%). Twenty-five patients out of pT3 had a bilateral ECE. The ROC curve showed an AUC of 0.80 (95% CI 0.74-0.85). Sensitivity and specificity were 77% and 69%, respectively. The model showed an acceptable calibration with tendency towards overestimation. CONCLUSIONS: From the current EV, the PRECE displays a good predictive performance to discriminate between cases with and without ECE; despite preliminary, outcomes may support the generalizability of the model in dataset other than the development one.
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