Evaluation of prediction models for the staging of prostate cancer

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Title: Evaluation of prediction models for the staging of prostate cancer
Authors: Boyce, Susie
Fan, Yue
Watson, R. William
Murphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/8217
Date: 15-Nov-2013
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 clinicians 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.
Funding Details: Irish Research Council
Type of material: Journal Article
Publisher: BioMed Central
Copyright (published version): 2013 the Authors
Keywords: Machine learningStatisticsPrediction modelsModel evaluationDiscriminationCalibrationProstate cancer
DOI: 10.1186/1472-6947-13-126
Language: en
Status of Item: Peer reviewed
Appears in Collections:Conway Institute Research Collection
Mathematics and Statistics Research Collection
Biomolecular and Biomedical Science Research Collection
Medicine Research Collection
Insight Research Collection

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