Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

I. El Naqa*, P. W. Grigsby, A. Apte, E. Kidd, E. Donnelly, D. Khullar, S. Chaudhari, D. Yang, M. Schmitt, Richard Laforest, W. L. Thorstad, J. O. Deasy

*Corresponding author for this work

Research output: Contribution to journalArticle

330 Scopus citations

Abstract

Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

Original languageEnglish (US)
Pages (from-to)1162-1171
Number of pages10
JournalPattern Recognition
Volume42
Issue number6
DOIs
StatePublished - Jun 1 2009

Keywords

  • Co-occurrence matrix
  • Image morphology
  • Intensity-volume histograms
  • Positron emission tomography
  • Treatment outcomes
  • Tumor heterogeneity
  • Tumor shape
  • Uptake values

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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