Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

Cheng-Jian Xu*, Arjen van der Schaaf, Cornelis Schilstra, Johannes A. Langendijk, Aart A. van t Veld

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

33 Citations (Scopus)

Abstract

PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models.

METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods.

RESULTS: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method.

CONCLUSIONS: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

Original languageEnglish
Pages (from-to)E677-E684
Number of pages8
JournalInternational Journal of Radiation Oncology Biology Physics
Volume82
Issue number4
DOIs
Publication statusPublished - 15-Mar-2012

Keywords

  • BMA
  • LASSO
  • NTCP
  • Stepwise
  • Xerostomia
  • LOGISTIC-REGRESSION
  • NECK-CANCER
  • SELECTION
  • RADIOTHERAPY
  • HEAD

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