D M Fanning1, F Yue, J M Fitzpatrick, R W G Watson. 1. UCD School of Medicine and Medical Science, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland. deirdre.fanning@ucd.ie
Abstract
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.
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.
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