TY  - JOUR
AU  - Frederic Sampedro
AU  - Sergio Escalera
AU  - Anna Domenech
AU  - Ignasi Carrio
PY  - 2014//
TI  - A computational framework for cancer response assessment based on oncological PET-CT scans
T2  - CBM
JO  - Computers in Biology and Medicine
SP  - 92–99
VL  - 55
KW  - Computer aided diagnosis
KW  - Nuclear medicine
KW  - Machine learning
KW  - Image processing
KW  - Quantitative analysis
N2  - 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.
UR  - http://dx.doi.org/10.1016/j.compbiomed.2014.10.014
N1  - HuPBA;MILAB
ID  - Frederic Sampedro2014
ER  -