PT Journal
AU Frederic Sampedro
   Sergio Escalera
   Anna Domenech
   Ignasi Carrio
TI A computational framework for cancer response assessment based on oncological PET-CT scans
SO Computers in Biology and Medicine
JI CBM
PY 2014
BP 92–99
VL 55
DI 10.1016/j.compbiomed.2014.10.014
DE Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis
AB 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.
ER