Study design

prospective

Study time

2017.11-2019.8

Country

Netherlands

Object

Inclusion criteria were (1) biopsy-proven prostate adenocarcinoma and (2) clinical indication for robot-assisted radical prostatectomy with ePLND based on either an ≥ 8% risk score of LNI based on the Memorial Sloan Kettering Cancer (MSKCC) nomogram or any high-risk feature (≥ T3, Gleason > 7, PSA > 20 ng/mL). Patients with distant metastases on PET for whom surgery was omitted were only included in case of histopathological confirmation. Only patients who underwent [18F]DCFPyL PET-CT at the Amsterdam UMC were included.

Sample size

76

Topic classification

Prediction，Supplementary Examition

Keypoint

Machine learning-based alysis of quantitative [18F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. 

Year