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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Inventory of prostate cancer predictive tools
Shahrokh F Shariat1, Pierre I Karakiewicz, Vitaly Margulis
1Department of Urology, University of Texas Southwestern Medical Centre, Dallas, Texas 75390-9110, USA. shahrokh.shariat@utsouthwestern.edu
Current Opinion in Urology
|April 3, 2008
Summary
This review inventories prostate cancer predictive tools, identifying 111 models, with 69 validated. These nomograms offer evidence-based, individualized predictions superior to clinician expertise for better patient care and clinical trial design.
Area of Science:
- Oncology
- Medical Informatics
Background:
- Prostate cancer management relies on accurate predictions of disease outcomes.
- Physicians require accessible, evidence-based tools for clinical decision-making.
Purpose of the Study:
- To create an inventory of published prostate cancer predictive tools (nomograms).
- To serve as a reference guide for physicians selecting appropriate predictive models.
- To describe patient populations, predicted outcomes, and characteristics of these tools.
Main Methods:
- A comprehensive literature search was conducted using MEDLINE for prostate cancer predictive tools.
- The search covered publications from January 1966 to November 2007.
- Identified tools were analyzed for input variables, prediction form, development data, outcomes, features, accuracy, and validation status.
Main Results:
- A total of 111 published prediction tools were identified.
- Only 69 of the 111 tools had undergone validation.
- Detailed characteristics of each model, including predictive accuracy and validation, are presented.
Conclusions:
- Decision rules like nomograms provide accurate, individualized predictions exceeding clinician expertise.
- Validated nomograms are crucial for evidence-based medicine and personalized patient care.
- Accurate risk stratification is essential for designing homogeneous patient groups in clinical trials for novel therapeutics.
