@Article{FredericSampedro2014,
author="Frederic Sampedro
and Sergio Escalera
and Anna Domenech
and Ignasi Carrio",
title="A computational framework for cancer response assessment based on oncological PET-CT scans",
journal="Computers in Biology and Medicine",
year="2014",
volume="55",
pages="92--99",
optkeywords="Computer aided diagnosis",
optkeywords="Nuclear medicine",
optkeywords="Machine learning",
optkeywords="Image processing",
optkeywords="Quantitative analysis",
abstract="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.",
optnote="HuPBA;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2606), last updated on Thu, 17 Nov 2016 11:38:32 +0100",
doi="10.1016/j.compbiomed.2014.10.014"
}