Novel predictive tools for Irish radical prostatectomy pathological outcomes: development and validation

• Published in: Irish journal of medical science Vol. 179; no. 2; pp. 187 - 195
• Main Authors: Fanning, D. M., Yue, F., Fitzpatrick, J. M., Watson, R. W. G.
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.06.2010
• Subjects: Adult; Aged; Area Under Curve; Confidence Intervals; Family Medicine; General Practice; Health Status Indicators; Humans; Internal Medicine; Ireland; Male; Medicine; Medicine & Public Health; Middle Aged; Neoplasm Staging; Nomograms; Original Article; Predictive Value of Tests; Prognosis; Prostate-Specific Antigen - analysis; Prostatectomy - methods; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - pathology; Prostatic Neoplasms - surgery; Reproducibility of Results; Retrospective Studies; ROC Curve; Sensitivity and Specificity; Treatment Outcome; Ireland; Tumour staging; Prostatectomy; Prognosis; Prostate cancer; Nomogram
• ISSN: 0021-1265; 1863-4362
• DOI: 10.1007/s11845-009-0393-6

Aims We developed and validated prostate cancer predictive models for Irish patients, allowing individualised predictions of radical prostatectomy pathological outcomes. Methods Retrospective review of the Irish Prostate Cancer Research Consortium database from 2003 to 2008 was performed. Two predictive models were formulated: a replica of the Partin tables ( n  = 169) and a look-up table based on PSA and biopsy Gleason Score ( n  = 253). Clinico-pathological parameters were compared to the Partin data set. Internal validation was performed. Results In total, 70% of patients were at clinical stage T1c. 5.8% had a PSA less than 4.1 ng/ml, whereas 25% of the Partin patients had a PSA in this range. Maximal predictive accuracy was seen for seminal vesicle invasion (area under the curve = 72%). Prediction of extra-prostatic extension and lymph node involvement was only equivalent to that of a chance phenomenon. Conclusions Our current results do not support the introduction of the formulated predictive models into routine clinical practice.