Development and validation of a UK-specific prostate cancer staging predictive model: UK prostate cancer tables

Thomas B. L. Lam, Olivier Regnier-Coudert, John McCall, Sam McClinton

Research output: Contribution to journalArticle

Abstract

Objectives
To construct new prostate cancer staging lookup tables based on a dataset collated by the British Association of Urological Surgeons (BAUS) and to validate them and compare their predictive power with Partin tables.

Patients and methods
Complete data on 1701 patients was collated between 1999 and 2008 across 57 UK centres. Lookup tables were created for prediction of pathological stage (PS) using PSA level, biopsy Gleason score (GS) and clinical stage, replicating Partin's original approach.

Tables were generated using logistic regression (LR) and bootstrap resampling methods and were internally validated and externally validated using concordance indices (CI) and area under the receiver operating characteristic curve (AUROC) respectively.

Results
The CI and AUROC analyses indicate that Partin tables performed poorly on UK data in comparison with US data.

The UK prostate cancer tables performed better than Partin tables but the predictive power of all models was relatively poor.

Conclusion
The study shows that the predictive power of Partin tables is reduced when applied to the UK population.

Models generated using LR methodology have fundamental limitations, and we suggest alternative modelling methods such as Bayesian networks.
Original languageEnglish
Pages (from-to)224-235
Number of pages12
JournalBritish Journal of Medical and Surgical Urology
Volume5
Issue number5
Early online date30 Jan 2012
DOIs
Publication statusPublished - Sep 2012

Fingerprint

Nomograms
Neoplasm Staging
Prostatic Neoplasms
ROC Curve
Logistic Models
Neoplasm Grading
Biopsy
Population

Keywords

  • Bayesian networks
  • logistic regression
  • lookup table
  • Partin table
  • prostate cancer
  • predictive model for staging

Cite this

Development and validation of a UK-specific prostate cancer staging predictive model : UK prostate cancer tables. / Lam, Thomas B. L.; Regnier-Coudert, Olivier; McCall, John; McClinton, Sam.

In: British Journal of Medical and Surgical Urology, Vol. 5, No. 5, 09.2012, p. 224-235.

Research output: Contribution to journalArticle

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abstract = "Objectives To construct new prostate cancer staging lookup tables based on a dataset collated by the British Association of Urological Surgeons (BAUS) and to validate them and compare their predictive power with Partin tables. Patients and methods Complete data on 1701 patients was collated between 1999 and 2008 across 57 UK centres. Lookup tables were created for prediction of pathological stage (PS) using PSA level, biopsy Gleason score (GS) and clinical stage, replicating Partin's original approach. Tables were generated using logistic regression (LR) and bootstrap resampling methods and were internally validated and externally validated using concordance indices (CI) and area under the receiver operating characteristic curve (AUROC) respectively. Results The CI and AUROC analyses indicate that Partin tables performed poorly on UK data in comparison with US data. The UK prostate cancer tables performed better than Partin tables but the predictive power of all models was relatively poor. Conclusion The study shows that the predictive power of Partin tables is reduced when applied to the UK population. Models generated using LR methodology have fundamental limitations, and we suggest alternative modelling methods such as Bayesian networks.",
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AU - McClinton, Sam

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N2 - Objectives To construct new prostate cancer staging lookup tables based on a dataset collated by the British Association of Urological Surgeons (BAUS) and to validate them and compare their predictive power with Partin tables. Patients and methods Complete data on 1701 patients was collated between 1999 and 2008 across 57 UK centres. Lookup tables were created for prediction of pathological stage (PS) using PSA level, biopsy Gleason score (GS) and clinical stage, replicating Partin's original approach. Tables were generated using logistic regression (LR) and bootstrap resampling methods and were internally validated and externally validated using concordance indices (CI) and area under the receiver operating characteristic curve (AUROC) respectively. Results The CI and AUROC analyses indicate that Partin tables performed poorly on UK data in comparison with US data. The UK prostate cancer tables performed better than Partin tables but the predictive power of all models was relatively poor. Conclusion The study shows that the predictive power of Partin tables is reduced when applied to the UK population. Models generated using LR methodology have fundamental limitations, and we suggest alternative modelling methods such as Bayesian networks.

AB - Objectives To construct new prostate cancer staging lookup tables based on a dataset collated by the British Association of Urological Surgeons (BAUS) and to validate them and compare their predictive power with Partin tables. Patients and methods Complete data on 1701 patients was collated between 1999 and 2008 across 57 UK centres. Lookup tables were created for prediction of pathological stage (PS) using PSA level, biopsy Gleason score (GS) and clinical stage, replicating Partin's original approach. Tables were generated using logistic regression (LR) and bootstrap resampling methods and were internally validated and externally validated using concordance indices (CI) and area under the receiver operating characteristic curve (AUROC) respectively. Results The CI and AUROC analyses indicate that Partin tables performed poorly on UK data in comparison with US data. The UK prostate cancer tables performed better than Partin tables but the predictive power of all models was relatively poor. Conclusion The study shows that the predictive power of Partin tables is reduced when applied to the UK population. Models generated using LR methodology have fundamental limitations, and we suggest alternative modelling methods such as Bayesian networks.

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