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Accurate prediction tools in prostate cancer require consistent assessment of included variables

Jäderling, Fredrik; Nyberg, Tommy; Blomqvist, Lennart; Bjartell, Anders LU ; Steineck, Gunnar and Carlsson, Stefan (2016) In Scandinavian Journal of Urology2013-01-01+01:00 50(4). p.260-266
Abstract

Objective: The aim of this study was to create a preoperative prediction model predicting extraprostatic tumour growth in men with clinically organ-confined disease from a prospectively collected Swedish cohort. Materials and methods: The study used data from 3386 men in the prospective multi-centre Laparoscopic Prostatectomy Robot Open (LAPPRO) trial, with 14 participating urological departments. External validation was performed using a cohort of 634 men from the largest study centre with patients who underwent surgery before and after the inclusion period of the LAPPRO study. External validation of the updated Partin table was used for comparison. The prediction models were created by multivariable logistic regression. Nomogram... (More)

Objective: The aim of this study was to create a preoperative prediction model predicting extraprostatic tumour growth in men with clinically organ-confined disease from a prospectively collected Swedish cohort. Materials and methods: The study used data from 3386 men in the prospective multi-centre Laparoscopic Prostatectomy Robot Open (LAPPRO) trial, with 14 participating urological departments. External validation was performed using a cohort of 634 men from the largest study centre with patients who underwent surgery before and after the inclusion period of the LAPPRO study. External validation of the updated Partin table was used for comparison. The prediction models were created by multivariable logistic regression. Nomogram prediction performance, internal, internal–external and external validation are presented as the area under the receiver operating characteristic curve (AUC). Results: The nomogram reached a prediction performance with an AUC of 0.741, with internal and external validation of 0.738 and 0.698, respectively. Internal–external validation showed great divergence between centres, with AUCs ranging from 0.476 to 0.892, indicating inconsistencies in pathological staging or one or more of the included variables in the regression model. When including centre as a variable in the multivariable model it was significantly associated with the outcome of pT3 (p < 0.001). AUC for external validation of the Partin table was 0.694. Conclusions: Accurate prediction tools in prostate cancer require consistent assessment of included variables, and local validation is needed before the use of such tools in clinical practice.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Clinically organ-confined disease, extraprostatic extension, multivariable logistic regression, nomogram, prediction model, preoperative variables, prostate cancer, validation
in
Scandinavian Journal of Urology2013-01-01+01:00
volume
50
issue
4
pages
7 pages
publisher
Taylor & Francis
external identifiers
  • scopus:84977266775
  • wos:000379024000004
ISSN
2168-1805
DOI
10.3109/21681805.2016.1145736
language
English
LU publication?
yes
id
d65854ed-bcde-4068-b711-bc6aae4a5d08
date added to LUP
2017-01-23 14:25:23
date last changed
2017-09-18 11:33:19
@article{d65854ed-bcde-4068-b711-bc6aae4a5d08,
  abstract     = {<p>Objective: The aim of this study was to create a preoperative prediction model predicting extraprostatic tumour growth in men with clinically organ-confined disease from a prospectively collected Swedish cohort. Materials and methods: The study used data from 3386 men in the prospective multi-centre Laparoscopic Prostatectomy Robot Open (LAPPRO) trial, with 14 participating urological departments. External validation was performed using a cohort of 634 men from the largest study centre with patients who underwent surgery before and after the inclusion period of the LAPPRO study. External validation of the updated Partin table was used for comparison. The prediction models were created by multivariable logistic regression. Nomogram prediction performance, internal, internal–external and external validation are presented as the area under the receiver operating characteristic curve (AUC). Results: The nomogram reached a prediction performance with an AUC of 0.741, with internal and external validation of 0.738 and 0.698, respectively. Internal–external validation showed great divergence between centres, with AUCs ranging from 0.476 to 0.892, indicating inconsistencies in pathological staging or one or more of the included variables in the regression model. When including centre as a variable in the multivariable model it was significantly associated with the outcome of pT3 (p &lt; 0.001). AUC for external validation of the Partin table was 0.694. Conclusions: Accurate prediction tools in prostate cancer require consistent assessment of included variables, and local validation is needed before the use of such tools in clinical practice.</p>},
  author       = {Jäderling, Fredrik and Nyberg, Tommy and Blomqvist, Lennart and Bjartell, Anders and Steineck, Gunnar and Carlsson, Stefan},
  issn         = {2168-1805},
  keyword      = {Clinically organ-confined disease,extraprostatic extension,multivariable logistic regression,nomogram,prediction model,preoperative variables,prostate cancer,validation},
  language     = {eng},
  month        = {07},
  number       = {4},
  pages        = {260--266},
  publisher    = {Taylor & Francis},
  series       = {Scandinavian Journal of Urology2013-01-01+01:00},
  title        = {Accurate prediction tools in prostate cancer require consistent assessment of included variables},
  url          = {http://dx.doi.org/10.3109/21681805.2016.1145736},
  volume       = {50},
  year         = {2016},
}