Development of a model to predict prostate cancer at the apex (PCAP model) in patients undergoing robot-assisted radical prostatectomy.
World J Urol
; 38(4): 813-819, 2020 Apr.
Article
em En
| MEDLINE
| ID: mdl-31435731
ABSTRACT
PURPOSE:
To develop a model based on preoperative variables to predict apical prostate cancer.METHODS:
We performed a retrospective analysis of 459 patients who underwent a robotic assisted radical prostatectomy (RALP) between January 2016 and September 2017. All patients had a preoperative biopsy and mpMRI of the prostate. Significant apical pathology (SAP) was defined as those patients who had a dominant nodule at the apex with a Gleason score > 6 and/or ECE at the apex. Binary logistic regression analyses were adopted to predict SAP. Variables included in the model were PSA, apical lesions prostate imaging reporting and data system (PI-RADS) score and apical biopsy Gleason score. The area under the curve (AUC) of the model was computed.RESULTS:
A total of 121 (43.2%) patients had SAP. On univariable analysis, all apex-specific variables investigated emerged as predictors of SAP (all p < 0.05). On multivariable analysis PSA and apical PI-RADS score > 3 (all p < 0.05) emerged as significant predictors of SAP. The AUC of the model was 0.722.CONCLUSION:
Patients with PI-RADS 3, 4 or 5 lesions at the apex were three times as more likely to have true SAP compared to those who have PI-RADS < 3 or negative mpMRI prior to undergoing RALP.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Próstata
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Prostatectomia
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Neoplasias da Próstata
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Procedimentos Cirúrgicos Robóticos
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Modelos Teóricos
Tipo de estudo:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article