Detection of Prostate Cancer Relapse With Prostate Specific Antigen Monitoring at Levels of 0.001 to 0.1 micro g./l
Yu H, Diamandis E, Wong P, Nam R, Trachtenberg J. Detection of Prostate Cancer Relapse With Prostate Specific Antigen Monitoring at Levels of 0.001 to 0.1 micro g./l. Journal Of Urology 1997, 157: 913-918. PMID: 9072598, DOI: 10.1016/s0022-5347(01)65082-1.Peer-Reviewed Original ResearchConceptsProstate-specific antigenPositive surgical marginsPreoperative prostate-specific antigenSerum prostate-specific antigenSerial serum samplesBiochemical relapseSurgical marginsClinicopathological featuresRadical prostatectomyTumor volumeLogistic regression modelsPSA changeGreater preoperative prostate specific antigenPostoperative serum prostate-specific antigenHigher preoperative prostate-specific antigenPostoperative prostate-specific antigenUnconditional logistic regression modelsSerum samplesProstate-specific antigen monitoringSerum PSA changesSubset of patientsUnivariate logistic regression modelYear of surgeryLength of followupConventional prostate specific antigenDetection of Prostate Cancer Relapse With Prostate Specific Antigen Monitoring at Levels of 0.001 to 0.1 micro g./l
Yu H, Diamandis E, Wong P, Nam R, Trachtenberg J. Detection of Prostate Cancer Relapse With Prostate Specific Antigen Monitoring at Levels of 0.001 to 0.1 micro g./l. Journal Of Urology 1997, 157: 913-918.. DOI: 10.1097/00005392-199703000-00047.Peer-Reviewed Original ResearchProstate-specific antigenPositive surgical marginsPreoperative prostate-specific antigenSerum prostate-specific antigenSerial serum samplesBiochemical relapseMicro g.Clinicopathological featuresSurgical marginsRadical prostatectomyTumor volumeLogistic regression modelsPSA changeGreater preoperative prostate specific antigenPostoperative serum prostate-specific antigenHigher preoperative prostate-specific antigenPostoperative prostate-specific antigenUnconditional logistic regression modelsSerum samplesProstate-specific antigen monitoringSerum PSA changesUnivariate logistic regression modelYear of surgerySubset of patientsLength of followup