A computational framework for cancer response assessment based on oncological PET-CT scans

Frederic Sampedro, Sergio Escalera, Anna Domenech, Ignasi Carrio

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

© 2014 Elsevier Ltd. In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks.
Original languageEnglish
Pages (from-to)92-99
JournalComputers in Biology and Medicine
Volume55
DOIs
Publication statusPublished - 1 Dec 2014

Keywords

  • Computer aided diagnosis
  • Image processing
  • Machine learning
  • Nuclear medicine
  • Quantitative analysis

Fingerprint Dive into the research topics of 'A computational framework for cancer response assessment based on oncological PET-CT scans'. Together they form a unique fingerprint.

  • Cite this