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Related Experiment Video

Updated: Jun 21, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

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

D M Fanning1, F Yue, J M Fitzpatrick

  • 1UCD School of Medicine and Medical Science, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland. deirdre.fanning@ucd.ie

Irish Journal of Medical Science
|July 15, 2009
PubMed
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Developed prostate cancer predictive models for Irish patients showed limited accuracy. The models are not recommended for routine clinical use due to insufficient predictive power for key pathological outcomes.

Area of Science:

  • Urology
  • Oncology
  • Medical Informatics

Background:

  • Accurate prediction of prostate cancer pathological outcomes is crucial for treatment decisions.
  • Existing predictive models, like the Partin tables, may require validation and adaptation for specific populations.
  • The Irish Prostate Cancer Research Consortium database provides a valuable resource for such studies.

Purpose of the Study:

  • To develop and validate predictive models for prostate cancer pathological outcomes in Irish patients.
  • To assess the performance of a Partin tables replica and a novel look-up table model.
  • To enable individualized predictions of radical prostatectomy findings.

Main Methods:

  • Retrospective analysis of the Irish Prostate Cancer Research Consortium database (2003-2008).

Related Experiment Videos

Last Updated: Jun 21, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

  • Formulation of two predictive models: a Partin tables replica (n=169) and a PSA/Gleason Score look-up table (n=253).
  • Internal validation comparing clinico-pathological parameters against established data.

    • Main Results:

    • 70% of patients presented at clinical stage T1c.
    • The model demonstrated maximal predictive accuracy for seminal vesicle invasion (AUC=72%).
    • Predictions for extra-prostatic extension and lymph node involvement showed accuracy no better than chance.

    Conclusions:

    • The developed predictive models for Irish prostate cancer patients did not achieve sufficient accuracy for clinical implementation.
    • Current models are not recommended for routine use in predicting radical prostatectomy pathological outcomes.
    • Further research may be needed to refine predictive models for specific patient cohorts.