Evaluation of prediction models for the staging of prostate cancer

Hdl Handle:
http://hdl.handle.net/10147/305684
Title:
Evaluation of prediction models for the staging of prostate cancer
Authors:
Boyce, Susie; Fan, Yue; Watson, Ronald W; Murphy, Thomas B
Citation:
BMC Medical Informatics and Decision Making. 2013 Nov 15;13(1):126
Issue Date:
15-Nov-2013
URI:
http://dx.doi.org/10.1186/1472-6947-13-126; http://hdl.handle.net/10147/305684
Abstract:
Abstract Background There are dilemmas associated with the diagnosis and prognosis of prostate cancer which has lead to over diagnosis and over treatment. Prediction tools have been developed to assist the treatment of the disease. Methods A retrospective review was performed of the Irish Prostate Cancer Research Consortium database and 603 patients were used in the study. Statistical models based on routinely used clinical variables were built using logistic regression, random forests and k nearest neighbours to predict prostate cancer stage. The predictive ability of the models was examined using discrimination metrics, calibration curves and clinical relevance, explored using decision curve analysis. The N = 603 patients were then applied to the 2007 Partin table to compare the predictions from the current gold standard in staging prediction to the models developed in this study. Results 30% of the study cohort had non organ-confined disease. The model built using logistic regression illustrated the highest discrimination metrics (AUC = 0.622, Sens = 0.647, Spec = 0.601), best calibration and the most clinical relevance based on decision curve analysis. This model also achieved higher discrimination than the 2007 Partin table (ECE AUC = 0.572 & 0.509 for T1c and T2a respectively). However, even the best statistical model does not accurately predict prostate cancer stage. Conclusions This study has illustrated the inability of the current clinical variables and the 2007 Partin table to accurately predict prostate cancer stage. New biomarker features are urgently required to address the problem clinician’s face in identifying the most appropriate treatment for their patients. This paper also demonstrated a concise methodological approach to evaluate novel features or prediction models.
Item Type:
Article
Language:
en
Keywords:
PROSTATE CANCER

Full metadata record

DC FieldValue Language
dc.contributor.authorBoyce, Susieen_GB
dc.contributor.authorFan, Yueen_GB
dc.contributor.authorWatson, Ronald Wen_GB
dc.contributor.authorMurphy, Thomas Ben_GB
dc.date.accessioned2013-11-22T12:59:39Z-
dc.date.available2013-11-22T12:59:39Z-
dc.date.issued2013-11-15-
dc.identifier.citationBMC Medical Informatics and Decision Making. 2013 Nov 15;13(1):126en_GB
dc.identifier.urihttp://dx.doi.org/10.1186/1472-6947-13-126-
dc.identifier.urihttp://hdl.handle.net/10147/305684-
dc.description.abstractAbstract Background There are dilemmas associated with the diagnosis and prognosis of prostate cancer which has lead to over diagnosis and over treatment. Prediction tools have been developed to assist the treatment of the disease. Methods A retrospective review was performed of the Irish Prostate Cancer Research Consortium database and 603 patients were used in the study. Statistical models based on routinely used clinical variables were built using logistic regression, random forests and k nearest neighbours to predict prostate cancer stage. The predictive ability of the models was examined using discrimination metrics, calibration curves and clinical relevance, explored using decision curve analysis. The N = 603 patients were then applied to the 2007 Partin table to compare the predictions from the current gold standard in staging prediction to the models developed in this study. Results 30% of the study cohort had non organ-confined disease. The model built using logistic regression illustrated the highest discrimination metrics (AUC = 0.622, Sens = 0.647, Spec = 0.601), best calibration and the most clinical relevance based on decision curve analysis. This model also achieved higher discrimination than the 2007 Partin table (ECE AUC = 0.572 & 0.509 for T1c and T2a respectively). However, even the best statistical model does not accurately predict prostate cancer stage. Conclusions This study has illustrated the inability of the current clinical variables and the 2007 Partin table to accurately predict prostate cancer stage. New biomarker features are urgently required to address the problem clinician’s face in identifying the most appropriate treatment for their patients. This paper also demonstrated a concise methodological approach to evaluate novel features or prediction models.-
dc.language.isoenen
dc.subjectPROSTATE CANCERen_GB
dc.titleEvaluation of prediction models for the staging of prostate canceren_GB
dc.typeArticleen
dc.language.rfc3066en-
dc.rights.holderSusie Boyce et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2013-11-20T17:44:20Z-
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