%0 Journal Article %T A computational framework for cancer response assessment based on oncological PET-CT scans %A Frederic Sampedro %A Sergio Escalera %A Anna Domenech %A Ignasi Carrio %J Computers in Biology and Medicine %D 2014 %V 55 %F Frederic Sampedro2014 %O HuPBA;MILAB %O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2606), last updated on Thu, 17 Nov 2016 11:38:32 +0100 %X 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. %K Computer aided diagnosis %K Nuclear medicine %K Machine learning %K Image processing %K Quantitative analysis %U http://dx.doi.org/10.1016/j.compbiomed.2014.10.014 %P 92–99